{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "26281547",
      "metadata": {},
      "source": [
        "# Cost-aware Bayesian Optimization\n",
        "\n",
        "This tutorial covers cost-aware Bayesian optimization, a situation in which the cost of evaluation is unknown but assumed to depend on the set or a subset of the optimization parameters. \n",
        "\n",
        "Note that cost-aware Bayesian optimization is a more general form of multifidelity Bayesian optimization: \n",
        "* In multi-fidelity Bayesian optimization, the fidelity parameters are typically known ahead of time, an the relationship between cost and performance is typically known e.g., the highest fidelity parameters are the least noisy and the most costly. \n",
        "* In cost-aware Bayesian optimization, we do not know a-priori which parameters dictate cost, nor do we make any assumptions about the relationship between cost and performance. \n",
        "\n",
        "Cost-aware Bayesian optimization is well suited to any problem for which the user suspects there to be a heterogenous cost of evaluation. It can also be used as a simpler alternative to multifidelity optimization, although we recommend a dedicated multifidelity algorithm for more experienced users. In this tutorial, the acquisition function we use for cost-aware Bayesian optimization is expected improvement per unit (EIpu), which has the following formula:\n",
        "\n",
        "$$\n",
        "EIpu(x) = \\frac{EI(x)}{c(x)^\\alpha}\n",
        "$$\n",
        "\n",
        "$c(x)$ is a cost model that predicts the evaluation cost and $\\alpha \\in [0, 1]$ is a decay factor that reduces or increases the cost model's effect to prevent cheap points from dominating the optimization routine. We recommend starting $\\alpha$ at 1 and decreasing it to 0 as the optimization budget is exhausted. \n",
        "\n",
        "[1]: [Lee, Eric Hans, et al. Cost-aware Bayesian Optimization. International Conference on Machine Learning, AutoML Workshop. 2020. ](https://arxiv.org/pdf/2003.10870.pdf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0795fafc",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Install dependencies if we are running in colab\n",
        "import sys\n",
        "if 'google.colab' in sys.modules:\n",
        "    %pip install botorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "05735702",
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import time\n",
        "import torch\n",
        "import warnings\n",
        "\n",
        "from abc import ABC, abstractmethod\n",
        "\n",
        "from botorch.acquisition import AnalyticAcquisitionFunction, ExpectedImprovement\n",
        "from botorch.fit import fit_gpytorch_mll\n",
        "from botorch.models import SingleTaskGP\n",
        "from botorch.models.transforms import Log\n",
        "from botorch.optim import optimize_acqf\n",
        "from botorch.test_functions import Ackley\n",
        "from botorch.utils import standardize\n",
        "from botorch.utils.sampling import draw_sobol_samples\n",
        "\n",
        "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "tkwargs = {\n",
        "    \"device\": device,\n",
        "    \"dtype\": torch.double,\n",
        "}\n",
        "SMOKE_TEST = os.environ.get(\"SMOKE_TEST\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6f3faf8",
      "metadata": {},
      "source": [
        "# Cost Modeling\n",
        "\n",
        "The first thing we do in this tutorial is define a simple cost model, in which we make no assumptions other than a positive cost. We will use the mean of a GP for the cost model. To enforce positivity, we will model the log cost and then exponentiate when we perform predictions. Users can use more bespoke cost models should they have a better understanding of their problem. \n",
        "\n",
        "Having defined the cost model, we'll also generate some simple plots of a 1D synthetic problem for illustrative purposes, where the objective is the Ackley function and the cost is quadratic."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "e4aca71c",
      "metadata": {},
      "outputs": [],
      "source": [
        "class CostModel(torch.nn.Module, ABC):\n",
        "    \"\"\"\n",
        "    Simple abstract class for a cost model.\n",
        "    \"\"\"\n",
        "\n",
        "    @abstractmethod\n",
        "    def forward(self, X):\n",
        "        pass\n",
        "\n",
        "\n",
        "class CostModelGP(CostModel):\n",
        "    \"\"\"\n",
        "    A basic cost model that assumes the cost is positive.\n",
        "    It models the log cost to guarantee positive cost predictions.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, X, Y_cost):\n",
        "        assert torch.all(Y_cost > 0)\n",
        "        super().__init__()\n",
        "        gp = SingleTaskGP(train_X=X, train_Y=Y_cost, outcome_transform=Log())\n",
        "        mll = ExactMarginalLogLikelihood(likelihood=gp.likelihood, model=gp)\n",
        "        fit_gpytorch_mll(mll)\n",
        "        self.gp = gp\n",
        "\n",
        "    def forward(self, X):\n",
        "        return torch.exp(self.gp(X).mean)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "12bcb738",
      "metadata": {},
      "outputs": [],
      "source": [
        "def synthetic_objective_with_cost(x):\n",
        "    dim = 1\n",
        "    f = Ackley(dim)  # synthetic objective is the Ackley\n",
        "    fx = f(x).unsqueeze(1)\n",
        "    cx = 200 * (1.1 - x) ** 2  # synthetic cost is quadratric\n",
        "    return fx, cx\n",
        "\n",
        "\n",
        "# Generate training data\n",
        "dim = 1\n",
        "num = 4\n",
        "bounds = torch.tensor([[0] * dim, [1] * dim], **tkwargs)\n",
        "train_X = draw_sobol_samples(bounds=bounds, n=num, q=1, seed=111).squeeze(1)\n",
        "train_Y, cost_Y = synthetic_objective_with_cost(train_X)\n",
        "\n",
        "# Fit GP to data\n",
        "train_Y = standardize(train_Y)\n",
        "gp = SingleTaskGP(train_X=train_X, train_Y=train_Y)\n",
        "mll = ExactMarginalLogLikelihood(likelihood=gp.likelihood, model=gp)\n",
        "fit_gpytorch_mll(mll)\n",
        "\n",
        "# Fit cost model to data\n",
        "cost_model_gp = CostModelGP(train_X, cost_Y)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a500755e",
      "metadata": {},
      "source": [
        "# Plot the GP and the Cost Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "686cd4d2",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cost of Evaluation')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot GP\n",
        "X_preds = torch.linspace(0, 1, 100, **tkwargs).unsqueeze(1)\n",
        "Y_preds = gp.posterior(X_preds)\n",
        "Y_mean = Y_preds.mean.squeeze().detach().numpy()\n",
        "Y_var = Y_preds.variance.squeeze().detach().numpy()\n",
        "axes[0].plot(X_preds, Y_preds.mean.detach().numpy(), \"r\")\n",
        "axes[0].plot(train_X, train_Y, \"k^\")\n",
        "axes[0].fill_between(\n",
        "    X_preds.numpy()[:, 0], Y_mean - Y_var, Y_mean + Y_var, color=\"m\", alpha=0.5\n",
        ")\n",
        "axes[0].set_title(\"Gaussian Process Model\")\n",
        "axes[0].set_ylabel(\"Objective Value\")\n",
        "\n",
        "# Plot Cost Model\n",
        "cost_preds = cost_model_gp(X_preds)\n",
        "axes[1].plot(X_preds, cost_preds.detach().numpy())\n",
        "axes[1].plot(train_X, cost_Y, \"kv\")\n",
        "axes[1].set_title(\"Cost Model\")\n",
        "axes[1].set_ylabel(\"Cost of Evaluation\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2ca3d020",
      "metadata": {},
      "source": [
        "# Expected Improvement Per Unit\n",
        "\n",
        "Having defined the cost model, we can now define our EIpu acquisition function and plot it for different values of $\\alpha$. Note that when $\\alpha=0$, EIpu simply reduces to EI. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "73667f9d",
      "metadata": {},
      "outputs": [],
      "source": [
        "class ExpectedImprovementWithCost(AnalyticAcquisitionFunction):\n",
        "    \"\"\"\n",
        "    This is the acquisition function EI(x) / c(x) ^ alpha, where alpha is a decay\n",
        "    factor that reduces or increases the emphasis of the cost model c(x).\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model, best_f, cost_model, alpha=1):\n",
        "        super().__init__(model=model)\n",
        "        self.model = model\n",
        "        self.cost_model = cost_model\n",
        "        self.ei = ExpectedImprovement(model=model, best_f=best_f)\n",
        "        self.alpha = alpha\n",
        "\n",
        "    def forward(self, X):\n",
        "        return self.ei(X) / torch.pow(self.cost_model(X)[:, 0], self.alpha)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "4510d1e6",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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6swgNDcWRI0fw1FNP4YsvvsCbb76JwMBATJo0Cc8//7wwdvfu3aitrdVbD4yIiIjsl0KtqworERERObS9e/fivPPOw7Zt23DllVea/f2efvppvP/++8jJyRmyQHtycjKCg4Oxc+dOs8c1EuXl5YiNjcWnn35q8IovW9PQ0ICxY8fiL3/5C2666Sa5w9Hr0ksvhUKhwJdffil3KERERGRhrPFFREREstu0aROam5vx6aef9vV1dXVp1dbau3cvTp06haVLl1o4QuO98sormDx5st0mvQDAz88PDzzwAF544YW+kxatTWZmJr755hs8/fTTcodCREREMuCKLyIiItJi6RVfuhQWFmL58uW47rrrEBERgTNnzuCtt96Cn58f0tLSEBgYKEtcRERERGQ7WOOLiIiIrJK/vz9mzJiBf/7zn6iqqoKXlxcuvvhi/PnPf2bSi4iIiIgMwhVfRERERERERERkl1jji4iIiIiIiIiI7BITX0REREREREREZJeY+CIiIiIiIiIiIrvExBcREREREREREdklJr6IiIiIiIiIiMguMfFFRERERERERER2iYkvIiIiIiIiIiKyS0x8ERERERERERGRXWLii4iIiIiIiIiI7BITX2R39u7dC4VCgb179w772s8++8z0gRERkV3gPENERObEeYbItJj4IrJSWVlZ2LRpE+bPnw93d3coFAoUFhYa9RqZmZlYuXIlvL29ERAQgOuvvx5VVVXmCZiIiGxOaWkp1q1bh1GjRsHX1xdr165Ffn6+QdcuXboUCoVC62vlypVmjpqIiGzFSOYZADhw4AAWLlwIT09PhIWF4fe//z2am5vNGDHZI2e5AyAi3Q4ePIi//e1vSExMREJCAlJSUoy6vqSkBIsXL4afnx+ee+45NDc348UXX0RqaiqOHDkCV1dX8wROREQ2obm5Geeddx4aGhrwyCOPwMXFBX/961+xZMkSpKSkIDAwcMjXGD16NDZv3iz0RUREmCtkIiKyISOdZ1JSUrBs2TIkJCTg5ZdfRklJCV588UXk5OTgu+++s9BPQfaAiS8iK7VmzRrU19fDx8cHL774otGJr+eeew4tLS04fvw4xowZAwCYPXs2LrjgAnzwwQe49dZbzRA1ERHZijfffBM5OTk4cuQIZs2aBQBYtWoVkpKS8NJLL+G5554b8jX8/Pxw3XXXmTtUIiKyQSOdZx555BH4+/tj79698PX1BQDExMTglltuwQ8//IALL7zQ7D8D2QdudSSbUVRUhDvvvBMTJkyAh4cHAgMDcdVVVxm0/W/p0qVISkrC8ePHMX/+fHh4eCA2NhZvvfWWzvEqlQrPPvssRo8eDXd3dyxbtgy5ubnCmJ9//hlXXXUVxowZAzc3N0RFRWHTpk1oa2szxY+LgIAA+Pj4DPv6zz//HJdccklf0gsAli9fjvHjx2Pr1q2mCJGIyK442jzz2WefYdasWX03IwAwceJELFu2zKh5oru7m9tOiIgMwHnG8HmmsbERO3fuxHXXXdeX9AKADRs2wNvbm/czZBSu+CKbcfToURw4cABXX301Ro8ejcLCQvz973/H0qVLkZGRAU9Pz0Gvr6urw0UXXYR169bhmmuuwdatW3HHHXfA1dUVN954ozD2z3/+M5RKJe6//340NDTgL3/5C6699locPny4b8y2bdvQ2tqKO+64A4GBgThy5Ahee+01lJSUYNu2bX3jOjo60NTUZNDPGBQUZMSfiH6lpaWorKzEzJkztb43e/ZsfPvttyZ5HyIie+JI84xKpcLp06e14gI088QPP/yApqamIR/AZGdnw8vLC52dnQgNDcUtt9yCxx9/HC4uLgbFQ0TkSDjPaBgyz6SmpqK7u1vrfsbV1RXJyck4efKkQfEQAQDURDaitbVVq+/gwYNqAOp//etffX179uxRA1Dv2bOnr2/JkiVqAOqXXnqpr6+jo0OdnJysDgkJUXd2dgrXJiQkqDs6OvrGvvrqq2oA6tTU1EHj2bx5s1qhUKiLior6+t5//301AIO+9HnhhRfUANQFBQWD/yH96ujRo1p/Lr3+7//+Tw1A3d7ebtBrERE5CkeaZ6qqqtQA1E899ZTWe7zxxhtqAOozZ84M+ud14403qp988kn1559/rv7Xv/6lXrNmjRqAet26dYNeR0TkqDjPaBgyz2zbtk0NQL1v3z6t71111VXqsLAwvdcSSXHFF9kMDw+Pvv/u6upCY2Mj4uPjMWrUKJw4cQLXX3/9oNc7Ozvjtttu62u7urritttuwx133IHjx49j7ty5fd/buHGjUPx90aJFAID8/HwkJSVpxdPS0oK2tjbMnz8farUaJ0+e7NtiuGLFCuzcuXMEP7nxepcnu7m5aX3P3d29b4yu7xMROSpHmmcMnScG8+677wrt66+/HrfeeiveeecdbNq0Sfh5iYiI80wvQ+aZoa431XZMcgxMfJHNaGtrw+bNm/H++++jtLQUarW673sNDQ1DXh8REQEvLy+hb/z48QCAwsJCYaIYWBcLAPz9/QFolhf3Onv2LB5//HF8/fXXQr80nvDwcISHhw8Znyn1TmIdHR1a32tvbxfGEBGRhiPNM+aaJ+677z6888472LVrFxNfREQSnGc0DJlnhrqe9zJkDCa+yGb87ne/w/vvv4977rkH8+bNg5+fHxQKBa6++mqoVCqTvpeTk5PO/t7JqaenBxdccAFqa2vx4IMPYuLEifDy8kJpaSluuOEGIZ62tjaDJjIACAsLG3nwQN/EVFZWpvW9srIyBAQEcLUXEZGEI80zvfOAvnkC0NxgGSsqKgoAUFtba/S1RET2jvOMhiHzzFD3M8OZo8hxMfFFNuOzzz7Db3/7W7z00kt9fe3t7aivrzfo+nPnzqGlpUV4SpKdnQ1AcyyuMVJTU5GdnY0PP/wQGzZs6OvXtQR4y5Yt2Lhxo0GvO/Cpz0hERkYiODgYx44d0/rekSNHkJycbJL3ISKyJ440zyiVSkyePFnnPHH48GGMHTt2WCcL5+fnAwCCg4ONvpaIyN5xntEwZJ5JSkqCs7Mzjh07hnXr1vX1d3Z2IiUlRegjGgoTX2QznJyctBJDr732Gnp6egy6vru7G//4xz9w7733AtB8aP7jH/9AcHAwZsyYYXQsgJioUqvVePXVV7XGWqLGV15eHgAgLi6ur++KK67Ahx9+iOLi4r4n8Lt370Z2djY2bdpk1niIiGyRo80zV155JR566CEcO3as79SsrKws/Pjjj7j//vuFsWfOnIGnp2ff1pnGxka4ubkJq4fVajWeeeaZvpiIiEjEecbwecbPzw/Lly/Hxx9/jMcee6wvSfbRRx+hubkZV111ldHxkONi4otsxiWXXIKPPvoIfn5+SExMxMGDB7Fr1y4EBgYadH1ERASef/55FBYWYvz48diyZQtSUlLw9ttvG33s+sSJExEXF4f7778fpaWl8PX1xeeff661Nx4Yfo2vhoYGvPbaawCA/fv3AwBef/11jBo1CqNGjcLdd9/dN3bZsmUANHv7ez3yyCPYtm0bzjvvPPzhD39Ac3MzXnjhBUyePNngJzZERI7E0eaZO++8E++88w4uvvhi3H///XBxccHLL7+M0NBQ3HfffcLYhIQELFmyBHv37gUAnDhxAtdccw2uueYaxMfHo62tDV9++SX279+PW2+9FdOnTzc6HiIie8d5xvB5BgCeffZZzJ8/H0uWLMGtt96KkpISvPTSS7jwwguxcuVKo+Mhx8XEF9mMV199FU5OTvjkk0/Q3t6OBQsWYNeuXQY/Vfb398eHH36I3/3ud3jnnXcQGhqK119/HbfccovRsbi4uOB///sffv/732Pz5s1wd3fHZZddhrvvvhtTp041+vV0qaurw2OPPSb09S6Ljo6OFhJfukRFReGnn37Cvffei4ceegiurq64+OKL8dJLL7G+FxGRDo42z/j4+GDv3r3YtGkTnnnmGahUKixduhR//etfh9yqGB0djUWLFuHLL79EeXk5lEolEhIS8NZbb+HWW281SXxERPaG84zh8wwATJ8+Hbt27cKDDz6ITZs2wcfHBzfddBM2b95skvjIcSjUpioqRGTFli5diurqaqSlpckdChER2SHOM0REZE6cZ4iGTyl3AERERERERERERObAxBcREREREREREdklJr6IiIiIiIiIiMguscYXERERERERERHZJa74IiIiIiIiIiIiu+QsdwCGUKlUOHfuHHx8fKBQKOQOh4jI5qnVajQ1NSEiIgJKJZ+BcJ4hIjItzjMizjNERKZlzDxjE4mvc+fOISoqSu4wiIjsTnFxMUaPHi13GLLjPENEZB6cZzQ4zxARmYch84xNJL58fHwAaH4gX19fmaMhIrJ9jY2NiIqK6vt8dXScZ4iITIvzjIjzDBGRaRkzz9hE4qt3ObCvry8nCiIiE+J2Cw3OM0RE5sF5RoPzDBGReRgyz3DDPRERERERERER2SUmvoiIiIiIiIiIyC4x8UVERERERERERHaJiS8iIiIiIiIiIrJLTHwREREREREREZFdYuKLiIiIiIiIiIjsEhNfRERERERERERkl5j4IiIiIiIiIiIiu8TEFxERERERERER2SUmvoiIiIiIiIiIyC4x8UVERERERERERHbJ6MTXvn37sHr1akREREChUOCrr74a8pq9e/di+vTpcHNzQ3x8PD744INhhEpERI6A8wwREZkT5xkiIsdidOKrpaUFU6dOxRtvvGHQ+IKCAlx88cU477zzkJKSgnvuuQc333wzvv/+e6ODJSLTKy4G9uwBMjKAlha5oyFDvH/yfbxz/B38kPcDzlSfQXt3u9whmRTnGSL70tAA/PwzcPw4UF0NqNWGX9ut6saJshP4uehndPZ0mi9IciicZ4jsS3NbJz75PhNvfn4ah9PPobOrR+6QyMoo1Gpjfv2QXKxQ4Msvv8Sll16qd8yDDz6I7du3Iy0tra/v6quvRn19PXbs2GHQ+zQ2NsLPzw8NDQ3w9fUdbrhENMCpU8DjjwNffy32R0QAN94IPPoo4OYmT2w0uHGvjUNubW5f+z9X/AdXJ11t1GvYyucq5xki21VTAzz/PPD660BbW3+/tzdw8cXAX/4CjBmjfV1LZwveOvYWvsv9DodKDqGlS/NUJtovGk8seQLXT70ezkpnC/0UNFy28rnKeYbIdrV3duP2Zw7i49dj0VM3uv8byi54x57BY0904YHrp8sXIJmVMZ+rZq/xdfDgQSxfvlzoW7FiBQ4ePKj3mo6ODjQ2NgpfRGQalZXA1VcDycnaSS8AOHcOeOYZYNEioKjI4uHREFRqFc42nBX6ov2iZYrGOnCeIbIu3d3As88CsbHACy+ISS8AaG4GtmwBpk8HpDmD8uZyzHt3Hu7feT92F+zuS3oBQFFDEW78+kYkvpGI73O50oYsh/MMkfV58u2j8B1dgg+fXiQmvQBA5YLmvMl4cEMyztu4lyvAyPyJr/LycoSGhgp9oaGhaGxsRJv0N6Ffbd68GX5+fn1fUVFR5g6TyCHU1gKLF2tuOIZy9CgwbRrAVfzWpbKlUmu7zxg/HUsmHAjnGSLroVYDt9yiWTXc1DT42Joa4KKLNKuP1WqgqL4Ii95fhNTK1EGvy6nNwapPVmFHrmErbYhGivMMkXXZ9NIB/Om2WeiqihlipBJ7P1iKiBknkVNSY4nQyEpZ5amODz/8MBoaGvq+iouL5Q6JyOZ1dACXXgpkZWl/z8VF9zX19cbVYiHzK6oXl+G5KF0Q7hMuUzS2i/MMkXk88wygq+a3Uqn5klKrNTXAsmuysOj9RcI27sGooca1X1yr9ZlIZC04zxCZx98/P41XHtKzfdGpQ2d3TYUbLvvsIrR2tZoxMrJmZk98hYWFoaKiQuirqKiAr68vPDw8dF7j5uYGX19f4YuIhk+lAjZu1BQXHsjPD3j6ac1T9+Zmzffj4/u//+ijwMqVlo2VBlfUIN7kRflFQamwymcYFsN5hsg6fPyxZvXWQAoFcN11mocu7e1Abq7mIUyvOXOAx59pwoqPV6C4UUwMxPnH4cv1X6Ly/krsuHYHZkbMFL5f21aLK7ddiY5u3Tc6RKbCeYbIOuw8Uoi7NkQC3e5Cv1dMOp57/zh6Ol2Rll+FlbfvBZTdmm+6NgHrrkR6wxH83w//Z/mgySqY/W5p3rx52L17t9C3c+dOzJs3z9xvTUS/evdd4D//EfsiIoDUVE1yy8cH8PICFi4Ejh0DrrgCWLYMeOIJeeIl/aSrGxy9vhfAeYbIGhQWag5GGUihAL78EvjoI81DFRcXIC4O+OIL4KWXgNBQYOtW4On9j2kl9ZNCkvDzxp9x6cRLEewVjBXxK3Dk5iO4IuEKYdyxc8ew6ftNZv7pyNFxniGSX21rHVZf0QJ1a6DQn7hyHxrzEvHwDTOgVCowKTYY3/19Kf72n3QofcuBNTcBQdkAgDePvYlvsr+RI3ySmdGJr+bmZqSkpCAlJQWA5njflJQUnD2rKbb88MMPY8OGDX3jb7/9duTn5+OBBx7AmTNn8Oabb2Lr1q3YtIm/pBBZyoYNwLXX9re9vYHt2wFd5Sb8/IBt24CvvgKcnCwWIhlIWtjeHut7cZ4hsj0xMcCbb4rzxquvAmvXao9VKIB779Ws/qpyPo7XjrwmfH96+HTs/e1erW3cCoUC7619D+MCxgn9fz/2d97IkFE4zxDZnj/++Ag6Lr4W8Cnt6wtOPorj/50PpVKhNf5366bi6KlGeCSL88PG/25EeXO52eMl62J04uvYsWOYNm0apk2bBgC49957MW3aNDz+69r2srKyvkkDAGJjY7F9+3bs3LkTU6dOxUsvvYR//vOfWLFihYl+BCIaipub5on7o49qbkq2bdOc6qiPQqFJjpH1ka6KsMcVX5xniGzTzTdrHqr4+ACbNgG/+93g4909u3HrN7dCpVb19bk5uWHLlVsQ6Bmo8xpfN198vu5zeDiL28v+9NOfoGZRSjIQ5xki25JemY63T7wNhJ8Cbp4DhJ6CR1QmTu9OgLurs97rpseMxysrXxH6qlurccNXN3DOcDAKtQ38jTc2NsLPzw8NDQ3cH080QtnZwPjxckdBwzX1rak4XXG6r/3umndx47QbB7lCN36uivjnQWQ6eXmaFWBDrRp+9dCruOf7e4S+Z857Bn9c/Mch3+OfJ/6JW/53i9D3w3U/4IK4C4yMlsyFn6si/nkQDd/Kj1fi+7z+o+bdu0Pw8/XHMXP86CGvVavVuGLrFfjyzJdC/9dXf43VE1abPFayHGM+Vx27IjKRAxpJ0qukBLj7buDoUdPFQ8ZhjS8isnZxcUMnvSpbKvHonkeFvoSgBNw9/f/wt78Bf/vb4NdvTN6I+IB4oe+5X54bTrhERGTFduTuEJJeAPDgsjsMSnoBmm3y76x+BxE+EZoONYCclbjx5h6oVFa/BohMhIkvIhrS2bPAHXdobmbeeAN46im5I3JMDe0NaOhoEPrsscYXEdm/14+8jubO5v6OTg+cX/4NJsS74g9/0JwO2dCg/3onpRMeXPCg0Le3cC8OFB8wU8RERGRp3apu3PfDfUJfuHc4/m++caczBnoG4s/LngeyLgHeOQJ88h2qf7kUz7x3zJThkhVj4ouIhrR1K/DWW0Bnp6b9zTfAiRPyxuSIpIXtASDKT8cJBUREVqy1qxVvHn1T6Fsd8ge88cxYVFRo2g0NwGuv6bh4gOunXI9In0ihb/Mvm00ZKhERyei9k+8hoypD6Htu2XPwcvUy+rWuSrgaLrteA87N6uv7y3MeXPXlIJj4IrJDr78OrFsH7NsHmKKK3+23A0FBYt/TT4/8dck40sL2Yd5hcHd2lykaInJkWVnAnDnAv/4FtLcbd+2HKR+ipq1G6Pvzb67HlVeK415+GWhq0v86bs5uuH/+/ULfN9nf4FT5KeMCIiIiq9Pdo8I910wB9j4GNIUC0Jz6u2HqhiGu1M3d1Rkb7i4R+loKkvCXj/g03xEw8UVkZ1QqzRHy27YBS5YAU6YAu3aN7DW9vYH7xFXG+Oor4BTvLSyK9b2IyFq8+SZw5Ajw298CUVHA888bdl2PqgcvH3pZ6Lto3EVIDE7Eo2LJL9TVabbXD+aW6bcg0EM8AfLVw68aFgwREVmtFz46ibacucDep4C/ngU++zeemPsilIrhpzD+9sAcOAeKOyiee8aFq74cABNfRHbmhx+A3Nz+dloa4OY28te96y4gIEDs28wdJRYl3erI+l5EJIfmZuCDD/rb1dVAba1h1/4v+3/Irc0V+u6bp3myMnUqcOml4viXXhp8RZmXqxd+P+f3Qt/nmZ+jo7vDsICIiMgqvfpaT39D5Qq3mpm4JGnpiF7T090Fv7lDfJDclDsFr23l03x7x8QXkZ2RPh2fPBlYuHDkr+vjA2zaJPZ98YXmhocsQ7rVkSu+iEgOH38MNDb2txUKzQEohnjxwItCe1rYNJwXc15f+/HHxfHV1ZoVxoO5adpNUEDR127saMSO3B2GBURERFbnl9MlqDgxU+i7bEMZlEqFnisM98Yjc+DkL255fPWtZj2jyV4w8UVkRwoKgO3bxb6779bclJjCXXeJq8e6uoB//9s0r01D00p8jWLii4gsS63W1JEc6JJLgJiYoa89UnoE+4v3C333zbsPigGT1LRpwLJl4nXvvz/460b6RmJR9CKh79P0T4cOiIiIrNJ9z+ZCSFW4NeCvD0w3yWt7e7jikuvFlccFB6bjbMUgRwmTzWPii8iO/P3vYjF7Pz/g2mtN9/r+/sBll4l9A7e7kHmxxhcRyW3fPiA9Xey7+27Drn3v5HtCe7TvaKybtE5r3MaNYnvnTqCkRGuY4OpJVwvtr7O+Rktni2GBERGR1ahtbMPRbyYLfVNXpCAswNtk77F5UxKg7Orv6PLEo69xu6M9Y+KLyE60tgL//KfYt3Ej4GX8ab+Dkt6QnDzJIveW0NHdgbLmMqGPNb6IyNKkq73GjQOWLx/6uo7uDmxN3yr03T7jdrg4uWiNvewywNe3v61Wa06PHMwViVfASeHU127tasU32d8MHRgREVmVB145DnWreGjJ5odM+ztvQkwQwmccF/q+/NTfpO9B1oWJLyI78fnnmhOwBrrzTtO/z7JlwOjRYt9Q21Bo5EoatZc7cKsjEVlSRQXw5Zdi3513AkoDfpv8Nudb1LWLk9S1U3QvSfb0BNavF/vef19c0SwV4hWCZWPFPZLc7khEZHu2fOgntAMmHceqebEmf58bfivWgmnOm4xvD+ab/H3IOjDxRWQntmwR2xdeqHkSb2pOTsCGDWLfJ58AnZ2mfy/qJ63v5evmi1Huo+QJhogc0hdfAD0DDtny9ARuuMGwaz9O/VhoL45ejJhRMXrHS1cX5+YC+/frHttr/SQxW/ZdzndoaGfNFiIiW/HL6RI054vbHG+9vdss7/XoTdOh8K4S+p7+21k9o8nWMfFFZAfq6oAffhD7pMkpU5Le6FRXA99wR4lZsb4XEclN+oBlzRpg1Kihr6trq9Padnjd5OsGvWbuXGDCBLFvqNXFl028DC7K/q2THT0d+G/Wf4cOkIiIrMKf/5EntBUedXjsZtMUtZfydHfB9AvFopVHvp2Azq4ePVeQLWPii8gOfPWV5oTFXm5uwOrV5nu/ceOAhQvFPm53NK+zDeITKNb3IiJLKivTFLYfaJ12XXqdtmVsQ2dP/7JgVydXXJl45aDXKBTaq762bgWaBzlx3t/DHyvjVwp9W9K36BlNRETWZs/2YKE9bkEaPN21a0Gayh9/Hym0VY3h+POHJ8z2fiQfJr6I7MBWsV4wVq0SCwObg65tKN3mWYlM0N7qyBVfRGRJn38u1tjy9tbMNYb4+LS4zXH1+NXw9xi6iPD114v1w5ydtU+UlJJud/yx4Ee0d7cbFigREclm78mzaC1KFPo2Xudp1ve8bMk4eEZn9Hcou/D1z6zzZY+Y+CKycTU1wK5dYp+0KLA5XHUVsGAB8OyzQFoakJGhuSkh89BKfLGwPRFZkHSb49q1gLv70NcV1hfi57M/C33XTRl8m2OviAjN1vrf/x7YvRuorATmzBn8mlXjVkGB/oLF7d3tOFB8wKD3IyIi+Tz/DzHhpPCswT3XTDX7+67+zTlg0qfAFdcA/xeMwkl3oVvFp/n2hrepRDbuyy/FlVbu7sAll5j/fX18gF9+Mf/7kAZrfBGRXEpLtT/vDd3m+J/U/whtf3d/rIo3cKkYgHffNXgoACDAIwAzI2bi6LmjfX0783bi/NjzjXshIiKyqJ++DRXaExalw911sdnf9/kHxmGL2wV97Zo24EDxASyONv97k+VwxReRjauq0pys1eviizVbUMh+qNQqFDcWC32s8UVElpKbC0QOKIPi6wusWGHYtV9lfSW0r0q8Cm7ObqYLToflY5cL7Z35O836fkRENDIpRblo624R+m6+3sci7x09KhpTQ8WVZf89w4NR7A0TX0Q27uGHNds/tm4FrrwSuM6wHSRkQyqaK4TC0AC3OhKR5SxZApw9C/z8M/C73wG33qo5RGUoZU1lOFJ6ROgbqqi9KVww9gKhfaLsBGpaa8z+vkRENDzfnt0K3DYL+H0csOxhuMTvw11XTbbY+6+dsFZof5X1FdQDC1uSzWPii8gOeHlpam5t2wZceqnc0ZCpSet7uShdEOYdJlM0ROSIlErNab5/+xvwwguGXfNN9jdC29fNF0tilpghOtH8qPnwdOlfCq2GGrsLdpv9fYmIaHi2pv96UldAPrDoz7jllS1wd7VcVaa1E8XEV35dPtKrhjhNhWwKE19ERFZOWt9rjN8YKBX8+CYi6/Z19tdCe1X8Krg6uZr9fd2c3bRqs+zM43ZHIiJrVNxQjFMVp4S+dZMMLCRpItPCpiHKN0ro43ZH+8I7JyIaMbVac8T8c89pTtw6flzuiOwLT3QkIlvT0tmCXfnikcNrJqwZ0WtWVmqK3a9dC/zlL4OPlW533Jm/k9tWiIis0He53wntAI8ALByz0KIxKBQKzRzV5Q5kXwR8/Q+8+LtFFo2BzIunOhLRiK1cCfzwQ3/7q6+AGTNkC8funG04K7RZ2J6IrN3O/J1o727vazspnIw6zVHqlVeAe+/VPGgBgOJi4IEH9I+XJr6KGoqQV5eH+ID4YcdARESmtyN3h9C+MO5COCmdLB7HZPW1wF+eB7q8AAD1AE5kl2P6eJYXsQdc8UVEIzZZUnvyv1wZbFJaK778uOKLiKzb11niNsfF0Yvh7+E/7NdLTu5PegHAyZOagvv6JIUkadVC5HZHIiLr0tnTqbU6eCQPSUbiumUzAKduoe/F97NliYVMj4kvIht14YXALbcAn38ONDTIG8tasR4kUlMHvyEh40hrfDHxRUSW8NRTms/3t94CioqGHt+rR9WjVdheemKWsRYuBAICxL7t2/WPVygUWD52udC3M5+JLyIia/LW5+loenMH8NOjQOlMQKXAirgVssTi5e6K6JlpQt/uHe6yxEKmx8QXkQ0qKgJ27gT++U/gyiuBoCAgI0O+eObP174h2c0DtEyGNb6ISA7btgFffw3ccQcQEwO8/LJh1x0uPYyq1iqhb/WE1SOKxdkZWCVZBDDUPCPd7rincA9UatWI4iAiItP59xcNQMl8YM/TwDtH4bP1IEK9Q2WLZ/UlCqFdmZGA1vYumaIhU2Lii8gGfSfWgISfHzBhgjyxAICTE3D++WIfE1+m0dDegMaORqGPNb6IyNyKi4E08cE35s417FrpNsekkCSM9R874piWiwu4sGcP0NOjf/yy2GVCu769HplVmSOOg4iITOPUL+FCe/L0Npki0bjtqnFiR6cPPvrujDzBkEkx8UVkg3aINSCxYoUm+SSnZeL9BXbvFuux0PBIV3sB0DpumYjI1KTzjL+/5tReQ0hP6FozfmSnOfaSzjO1tUBKiv7xkb6RWp+XB0sOmiQWIiIameNZZWgvFZ/cX3t5gJ7RlpE0NhjuEWJdry3/q5EpGjIlJr6IbExnp/ZqKun2DzlIb0jKy+XdfmkvpPW9wr3D4ebsJlM0ROQopCuLL7zQsAcsFc0VOF1xWuhbNc40k1RUFDBO8jB+qNXF86LmCe2DxUx8ERFZg9f/nSt2uDfgxtWJ8gQzQMLsMqF94sAoeQIhk2Lii8jG/PIL0Nws9l14oTyxDBQfr7kpGYjbHUeO9b2IyNI6O4Fd4iFbBj9g2V0gfvD7uPpgTqSBS8UMIN3uOGTia7Qk8cUVX0REVmHnD85COzI5A+6uznpGW86aVZ5CuyE3EZV1LTJFQ6bCxBeRjZFuP5k5EwgJkSeWgRQK429IaGjSFV+s70VE5nbwINDUJPatXGnYtdKTE5fGLIWLk4uJItNeXfzzz0BHh/7x86PmC+3M6kzUtdWZLB4iIjJee2c3SlPE1V0XXNgtUzSiWy+fCCgHFLTvccU7X7E+pK1j4ovIxkiTSYbejFiC9IZk716g2zrmMJt1tvGs0I7244ovIjIv6TwzbRoQasAhW2q1GjvzxMSX9GTFkTrvPM2Dll5tbZpEnT7JYclwdxaPoz9cetikMRERkXH+80MW0O4n9P3u2nF6RltWRJAPfMaKia6vvm3WM5psBRNfRDakvh44eVLsk66ykpP0ZMfGRuDYMXlisRfSFV9MfBGRue3dK7YNnWfOVJ9BaVOpeO1Y005SAQHA9Oli32Cri12dXDEjfIbQxzpfRETy2vZtldB2DcvD9PFhMkWjbdr8WqGdftiApz9k1Zj4IrIhv/winpTo5mb4KVuWEB4OJEpqUnK748iwxhcRWVJbG3BYsiBqyRLDrt2VLxYGi/SJxMSgiSaKrJ+uU4QHwzpfRETW5dhBL6E9bnqpnpHyuPJif6HdVjwBWWd5uqMtY+KLyIb89JPYnjsXcHfXPVYu0pUB0gLJZLj27naUN5cLfVzxRUTmdPiwprh9L6USWLjQsGul9b0uiLsAioH7Ek1EOs8cOaJZYayP9GTHw6WHoVKrTB4XERENrb2zG1WZE4S+ZefJX9R+oI2rEwCXgQXtlfjHZ9myxUMjx8QXkQ2Rbj8x9Cm8JUmfxB84ALS2yhOLrStpLNHqY3F7IjIn6TwzbRrg56dzqKCrpwt7C8WLl8eaZy/+ggWAq2t/u6dH+8HQQNIVX40djcioyjBLbERENLitu7OBDl+h76bL4mWKRjdvD1cEJYjzxI6dnXpGky2wrtQqEenV2AicOCH2LV0qSyiDWrIE8PfXrBBYtkzz5eEhd1S2SVrfy8/ND37uBtyBEhENkzSBZOgDlsOlh9HUKR4Faer6Xr08PYHFi4GWlv55Zt48/ePDfcIR7RctbB0/UHwASSFJZomPiIj027ZdUt8rJB9T4sbKFI1+cxa1YHtJNjB2NzB2FzqmFQM4IndYNExMfBHZiP37AdWAnRmurpqtjtbGzw+oqgKcnOSOxPaxvhcRWVJ7O3DokNhnaOJLeprjlNApCPU2XzHgHTuMm2fmRc0TPlMPlhzErTNuNUNkREQ0mCMHxCfiY6eVALC+xNfjD/pge3D/lsz8DqCypRIhXiEyRkXDxa2ORDZCuv1k9mzrXUnFpJdp8ERHIrKkI0c0ya9eCgWwaJFh1+4qEAs6mmubYy9j5xmtAvc82ZGIyOI6u3pQmSHW9zp/qXXeOEyPnAovF7EI/4HiAzJFQyPFxBeRjXjwQeCrr4B77gGSk4Hzz5c5IDI76Yov1vciInOaMwfYtw94+un+7YP+/kNf19rViiOl4vYPc21zHC5p4iurJgt1bXUyRUNE5JjSqk4DN80DLroTmLQF8KrAxkutb7UXADgrnTFn9Byhb//Z/TJFQyPFrY5ENiIgAFi7VvMFAGq1vPGQ+Z1tOCu0ueKLiMzJzU2zwmvRIuDRRw2fZw6VHEK3qruv7aRwwsIxBh4FaSHJYclwc3JDR09HX9/J8pM4P5ZPkYiILOWnor1ASKbma/bfEecfj5kTc+QOS68FUQvwY8GPfe39xUx82Squ+CKyUWY4IZ6sDGt8EZGcDJ1n9hXtE9rTw6fDx83HDBENn4uTCyaHThb6TpadlCkaIiLH9FOReILK0hgrPKJ+AOlDnONlx9He3a5nNFkzJr6IyKxUKiA1Fdi1a+ix1E+lVqG4oVjo44ovIrJG0sTX4ujFFo+hpATYulU8BEZqWtg0oX2i/ISekUREZGoqtUprvlgas1SeYAw0d/RcKBW/pkzafdCZtQRfH0qVNygaFm51JCKzOHkSeOQR4OBBoKEBGDMGKCoa+jrSKGsqQ5eqS+hjjS8isjYd3R04WCIWirdU4qujA9i4UXPq8dlfd4anpgJJSbrHSxNfXPFFRGQ5aZVpqGsXaysuibbuFV++br4ITXkJZQeWApWTAbUT3vPai3XWtZufDMAVX0RkFi4umuPmGxo07bNnNU/kyTDSbY6uTq4I9Q6VKRoiIt2OnTsmbPtQQIFFYww8CnKE3NzEpBegaeszPXy60M6qyUJrV6uZoiMiooGkheFjR8Uiyi9KpmgMF9AxHahIBtSa0ydPHvGUNyAaFia+iKzc2bPAd98BdTZ2+FRiIjBqlNg32A0JiYrqtU907FtqTURkQmo1sGULUFBg/MEp0m0rk0Mnw9/DgKMgTWTBArE92DwzOXSy8DmqUqtwuuK0mSIjIqKBvvyuEShLBno0CaT5UfPlDchASxY5Ce2qrHh09wyyr56sEu+iiKzcF18AF12kOdUxIQF44gm5IzKMUgnME0+PZ+LLCFqF7Vnfi4jMJDcXuPpqYOxYIDwcuPRSoLnZsGv3nZXU9xpj2fpexiS+PF08MTFootB3oox1voiILGHv6+uAf5wE/twAvL8HoXWXyh2SQX5zkfg7uLo1ADsOFcgUDQ0XE19EVu7ggNIpZ84ApaXyxWIsY25ISCRd8RUzKkaeQIjI7g2cZyoqgEOHAC+voa/rVnVrbV1ZYuETuqTzTH4+UF6ufzzrfBERWV722Vp0VcVqGl1eQNFSTIucJGtMhpqXFAmlX5nQt23HOZmioeFi4ovIyh0UawZrraKyZtIbklOnDF9F4OgKGwqFNld8EZG5HDoktufNAxSKoa87VX4KTZ1NQp+l6nv1mjwZ8PER+4yp83WynIkvIiJz+/eOPLHDpRWXL42XJxgjKZUKRCaKK7wOHJApGBo2Jr6IrFhpKVBcLPbNnStPLMMxezbgPODs2J4e4PBh+eKxJdIVX9GjmPgiIvMY7gOWn4p+EtoTAidY/BAOJyfteXGwxJd0xVdqZSq6err0jCYiIlPYta9FaPuNzYanu4tM0Rhv1lxxnihKHS1TJDRcTHwRWTHpU3g/P02dL1vh6QlME+8xmPgygFqt1qrxxa2ORGQOzc3AaUl9d0MfsEgL2y+Otmx9r17S1cWDzTPJYclCu7OnExlVGaYPioiI+mSc9BXaE6bWyxPIMF12YYjQ7qqKRV6pjZ085uCY+CKyYtKn8HPmaIrG25I5c8T2kSPyxGFLqlur0drVKvRxqyMRmcOxY4BqwOFUzs7AzJlDX6dSq/Dz2Z+FviXRlq3v1Us6z5w4AXTpWcTl7+Gv9SCB2x2JiMyns6sHdbnjhL7zF3nIFM3wXL40HnBuE/o+/SFPz2iyRjZ2C03kWKQrvmxpm2Ov2bPF9tGj8sRhS6SrvZwUToj0jZQpGiKyZ9IHLFOnalbrDiW7Jhu1bbVC36Joy9b36jVrlthubwfS0/WPZ4H7oX2W8RnWf7Yej/34GD469RHSKtPkDomIbNTXv+QBnWIxxt9cFCtTNMPj6e4C7zG5Qt+e/SxcbEuY+CKyUp2dmifxA9lSYfte0huSc+c0X6SftL7XaN/RcFY66xlNRDR8w63vdaBYrOw72nc0xviNMVFUxgkMBMaOFfsGe8jCAvdD++XsL9iavhXP/PwMNny1Ac/+/KzcIRGRjfpqZ4XQdg48i8ljQ/SMtl5jk8SHPekptrVqzdEx8UVkpVJSgI4OsU+6ncMWjB8P+Irb+rnqawjSFV8sbE9E5qBWD39l8cFiMWM2b7S8T2akD1kGm2ekK75SylOgUqv0jHZM2TXZQnt8wHiZIiEiWyetuxiZUKx7oJWbN9tJaFfm8PdzW8LEF5GVkj6FnzgR8PeXJ5aRUCqBGTPEPia+BldYXyi0Wd+LiMwhPx+oqhL7DF7xVSKu+JofNd9EUQ2PUYmvcDHx1dTZhLxa1moZSJr4mhA0QaZIiMjWnU0Ty3XMnN0tUyQjs/Z88edQNYbh2JkymaIhYzHxRWSlpE/hbXGbYy9jbkhIx4ovJr6IyAyk80xICBBrQNmV+vZ6rZMQrW3FV2oq0Name2y4dzhCvMRtNqmVqWaKzPZ0dHegoL5A6BsfyBVfRGS8vNI6dFaKe9HXLA+WKZqRuWB2NODWIPR9vuusTNGQsZj4IrJSw627Yo0G3pB4egLu7vLFYgukNb6kJ5AREZmCdJ6ZOxdQKIa+7lCJmDFzc3LTWkVladOn9596rFQCiYlAmZ4H8QqFAlNCpwh9LN7eL78uX2vr57iAcXpGExHp98l3YkF4OLdpTki0Qc5OSvjHDVgd7HsWGeeK9F9AVoXVkomsUGUlUCT5HLXF+l69Fi8G3nsPmDkTSEgAnPnJMyitrY6s8UVEZiBdfTvc+l4zI2bC1cnVRFENj7c38O67QHw8MG0a4OU1+Pik4CTsyt/V12biq590m2OoVyj83P1kioaIbNneAy1C2ycmB94eU/SMtn4XbDiBraceByKPAt6VaBt7AYB1codFBhjWiq833ngDMTExcHd3x5w5c3DkyJFBx7/yyiuYMGECPDw8EBUVhU2bNqG9vX1YARM5guPHxbanp+bpta0KCQE2bgQmT2bSaygN7Q1o6BCXUTviVkfOM0Tm1dkJnDol9s2ebdi11lbfq9cNNwALFw6d9AKApJAkoc3EVz9Hqe/FeYbI/DJOiScfjptcq2ekbVi32h+YsB3wrgQAHD13FGq1WuaoyBBGJ762bNmCe++9F0888QROnDiBqVOnYsWKFaisrNQ5/t///jceeughPPHEE8jMzMS7776LLVu24JFHHhlx8ET2qrUViIvrb0+bxoSRo5DW9wKAMX5jZIhEPpxniMyvshKYNEmcW6ZPH/q6HlUPDpeIR3TJXd9rOKSJr+yabHR0d+gZ7ViyarKEtj2e6Mh5hsgyGlTlQl2subNdZIxm5GZFigUl69vrkVubq2c0WROjE18vv/wybrnlFmzcuBGJiYl466234Onpiffee0/n+AMHDmDBggX4zW9+g5iYGFx44YW45pprhnyqQuTIrrgCyM0FamqAnTuBJ5+UOyKyFOk2x3DvcLg5u8kTjEw4zxCZ3+jRmtXFTU3AkSPAhx8adnJwelU6mjqbhL55UbaX+EoMFpdR96h7tBI+jkq64sseC9tzniEyv3NN59B+6aXAg/7A3eOBy3+Da1aHyR3WiET5RmkdjnL0HE/tsgVGJb46Oztx/PhxLF++vP8FlEosX74cB6UVUn81f/58HD9+vG9iyM/Px7fffouLLrpI7/t0dHSgsbFR+CJyRAEBwPLlmi9yDNLC9o5W34vzDJFlubtrDiDZsMGw8dL6XrGjYhHmbXs3Mj5uPloHh3C7o4a9J744zxBZxrFzxzT/oVQDQTnwnbUd8xMNODrYiikUCsyKEFd9HS1l4ssWGLV5qrq6Gj09PQgNDRX6Q0NDcebMGZ3X/OY3v0F1dTUWLlwItVqN7u5u3H777YMuDd68eTP+9Kc/GRMaEZFdkG51dLQTHTnPEFk3a63vNRyTgicJq2yZ+NLUmaxoqRD67K3GF+cZIss4fk4sWjw9fDqUimGVGLcqsyJmYXvO9r42V3zZBrP/y9u7dy+ee+45vPnmmzhx4gS++OILbN++HU8//bTeax5++GE0NDT0fRUXF5s7TCKygNZWYP9+4JVXgGuvBb78Uu6IrI/WiY4OWNjeWJxniCznQLGY+LK2+l49PUB6OvDBB8BddwEPP6x/LAvca5Ou9lIqlBjrP1amaKwH5xki4x0rOya0Z4bPlCkS05oZMQtoiAQyLwV2PYuDzz+K9s5uucOiIRi14isoKAhOTk6oqBCfBFVUVCAsTPcy98ceewzXX389br75ZgDA5MmT0dLSgltvvRV//OMfoVRq597c3Nzg5uZYNW2I7NXZhrN4+qenkVaVhpJ3X0bJwf6bpFGjgMsuky82ayRd8eVoiS/OM0TWq6qlSquIr7Wt+PrkE+C3v+1vR0cDmzfrHsvElzZp4it2VCxcnVxlisY8OM8QmZ9are7f6virmRH2kfiKc5sN/LWkr60C8N3BHFy2ZJx8QdGQjFrx5erqihkzZmD37t19fSqVCrt378a8ebqf+LW2tmpNBk5OTgDAoz+J7Fh7dzue3fcsJr4+Ef88+U8cKjmEEs//CmP2HWa9CylpjS9H2+rIeYbIekm3c3i6eGJy6GSZotFt2jSxXVQE1NbqHitNfBXUF6C5s9lMkdkGe6/vBXCeIbKEksYSVLaIp6TaS+JrYnQQnEaVCn3f/1KhZzRZC6NWfAHAvffei9/+9reYOXMmZs+ejVdeeQUtLS3YuHEjAGDDhg2IjIzE5l8fr61evRovv/wypk2bhjlz5iA3NxePPfYYVq9e3TdhEJGGWg1ceSUQFwfMnKn5io0FFAq5IzNOWmUaLttymfbxvmEp4rjTTtiZ+yMuiD/fcsFZsZbOFlS1Vgl9jlbcHuA8Q2Ruzz2nSQj1zjOTJgGuBizqOVIqnmA3I3wGnJVG/yppVhMnAm5uQEdHf19KCnC+jmlmYtBEKBVKqNSqvr6MqgzMjpxt/kCtVHatmPiaEGhf9b16cZ4hMq93PysCtn0KRBwDIo7BLzbfrrZNB40tQcWJyL720RPc6mjtjP5tZf369aiqqsLjjz+O8vJyJCcnY8eOHX0FIs+ePSs8EXn00UehUCjw6KOPorS0FMHBwVi9ejWeffZZ0/0URHbi3Dngiy/EvqwsYLwNPXCtbKnEyo9XorSpVPub4SfFdpcXLn7tD/jyrj/j4vEXWyZAK3a24axWn6NtdQQ4zxCZ27ZtmmTQ229r2i+/DGzaNPR1h0sPC21rTBC5uABJScDxATWV9SW+3J3dMS5gHLJqsvr60irTrPLnspSs6iyhbY8rvgDOM0TmtvPHLiB9veYLgDLxBBRP2NiT/EFMSGpDxYn+dn6mn3zBkEEUahtYn9vY2Ag/Pz80NDTA19dX7nCIzOa//wUuvbS/7esL1NUBOkpHWKVuVTcu/OhC7CncI/QroMDG5I3wcfPBm1f/EV0Nwf3fvOJqOE/9HLuu34UlMUssHLF12ZG7A6s+WdXXDvQIRPUD1WZ5L36uivjnQY6irU0zt3QPeDi9bx+waNHg16nVagS9EITatv59g1uu3IJ1k9aZKdLhu+UW4J//7G9fdx3w0Ue6x1659Up8nvl5X3vT3E14ecXLZo7QOqnVavhs9kFLV0tf367rd2HZ2GXDej1+ror450GOJGjKMdSk9m9tnLNuLw5tWSpfQCb28OuH8effzelrK9zr0d3iB6XSfpJ7tsCYz1UbuZ0mcgzHxBqQmDHDdpJeAPDI7ke0kl7Twqbh2K3H8O7ad/HKyldw/rxA8aLyaehWdeOO7XegW+XYy4S1TnR0wG2ORGRep0+LSS+FQrsuli75dflC0guwzhVfAJCcLLZPntQ5DAAL3A9U1lwmJL0A+13xRUTmo1KpUZsXK/QtmGtfBz1csjhSaKvbR+FAWome0WQNbOiWmsj+DdyaAWgSX7bi84zP8cKBF4S+SJ9I7LhuB6aHT+/rmzFd8rFTprnjyqzOxDvH3zF7nNZMmviKHRWreyAR0TBJH7BMnAh4ew99nbS+V4hXiNVuxZYm8s6c0ax004WJr37SwvaeLp6I9I3UM5qISLcDaaVQt4oPuteeN1qmaMxjXlIkFB51Qt/2fedkioYMwcQXkZVQq7VvSGbayOEnDe0NuO2b24Q+F6ULPlv3GUK8QoR+rZUF5cnArxuun9j7BBo7HPekR2niy9FOdCQi8xvuPCNNfM2OnA2FlZ68MmWKeChMTw+QpiefJU18lTWXoaa1xozRWS9pfa9xAeOgVPBWgYiM8+XuYqGt8KzFwin2lfhSKhXwiykU+g4c1fOEhawCZzMiK1FSAlSJB/rZTOLrpYMvoaZNvFH426q/Ye7ouVpjtRJfrSFAUwQAoKq1Cpt/3myuMK0eE19EZG7SlcUGJ77OSRJfEda5zRHQrGCTHgqjb7tjfEA8XJ3EIy3Tq9LNFJl1k6744jZHIhqOA0c6hHZAXL5d1r6KS2gQ2lnpHjJFQoZg4ovISkh/KffzA8bawKm/Fc0VePmgWAh4zYQ1uG3GbTrHx8YCPj6SzrL+bNhfD/1VKwHkKArqC4Q2tzoSkSm1twOZmWLf9Om6xw7U1dOFE2UnhD5rre/Vy9A6X85KZyQEJQh9jrrdMbuWiS8iGrncDC+hPW5Ss0yRmNesGS5CuzrPvla12RsmvoisREqK2E5OFrdqWKtnf35WKIarVCixedlmvVtglErtGxJlRf+dV0dPB57c+6QZIrVurV2tqGypFPq44ouITCk9XSxsDwBTpw59XVplGtq724W+WZGzTBiZ6UlXF0vn2IESgxOF9pnqM6YPyAZItzoy8UVEw1GbL9Z/nDvTVc9I27ZioVjOpac+EjnFtXpGk9yY+CKyEroSX9auoK4Abx17S+j77dTfat1ESElvSGLbLxfaW9K3oL693hQh2oyi+iKtPp7qSESmJJ1n4uN1rMDVQVrfKz4gHgEeAaYLzAyk88zp05paX7pIV3xlVmfqHmjHunq6kF+XL/RNCJwgUzREZKtO51VC1SwmhFYuDJcpGvNaOTcWcBbren25t0DPaJIbE19EVsIWE19P/vQkulRdfW1XJ1c8ufTJIa+T/mxd5ybDRdm/XLi9ux2fpn1qoihtg3R7Z7BnMLxdDThqjYjIQMOdZ3QVtrd20p+ttRXIztY5FBODJgptR1zxlV+Xjx61mBnkii8iMtbXP50VO1ybcf6MMfIEY2burs7wGi0+MNh3qEmmaGgoTHwRWYH6eqBA8oBAqwi8lcmvy8dHpz4S+u6adRfG+A09uUl/trOFTlg5+mqh772T7404Rlsire/FbY5EZGrSxJeh84wtFbbvFRICRESIffrqfCUEiyu+ShpL0NThWDcvWTXiNsdgz2D4e/jLFA0R2apfjoj1vHyi8uHi7CRTNOYXNUE83Cst1UXPSJIbE19EVuDUKbHt4gIkJOgeay3eOvYW1FD3tb1dvfHwwocNujYxUfMzRkUBa9YAjz8OXJN4nTDm6LmjDlVgmCc6EpE5qVTac40hK76aOpqQXimecmgLK74ATWLPzw9YsgS45x7tkx57jQsYB6VC/JVYmgiyd9ITHScEcZsjERkvI1Ws5xU9sU6mSCwjOVkNKDuBsBPAtHfRFfel3CGRHs5yB0BE2k/hJ00CXK24DmRbVxvePfmu0HfztJsR7BVs0PWurkBVleaGpFe36nzc90s4yprL+vreP/k+XlrxkklitnbSxBdPdCQiUyooAJoki5gMSXwdLzsuPORwVjojOcyAC63Av/+tqWE21EExbs5uGOs/Frm1uX19mVWZmBkx08wRWg+twvYB3OZIRMaryA0T2tOSbeCkrhG47SYPfOriDThrSr+UK5Ro7XoKni6eMkdGUlzxRWQFbK2+17aMbahtE08tuX3m7Ua9xsCkF6C5mfrt1N8KfR+d/ghdPV1wBNzqSETmJJ1ngoOBcAPqDR8/d1xoTw6ZDA8XD9MFZka+voafjiyt8+VoBe6lK9y44ouIjFVR14zOyhihb9m8IHmCsZDZMUlQuvTXR1SpVThdcVrGiEgfJr6IrMAllwC33QbMmQN4eFh/4uvNo28K7eVjl5vkl+SN0zYK7arWKmzP2T7i17UF3OpIROYUHw/83/8BF1wABAVp5hlDkkLHyo4JbXtdBSU92dHRCtxrbXXkiY5EZKRTpRnAeY8DCZ8B/rmAsgurF9n3DgZPF0+tg0BSK1JlioYGw62ORFbgiis0X4DmuPUuK17kdKLsBA6XHhb67px5p0lee3zgeCyIWoD9xfv7+t47+R4unXipSV7fWjV3NqO6tVroY+KLiExp6lTNFwCo1ZpTDg0hXfFlr4kvR17x1dDegIqWCqGPJzoSkbHy2o4DS57ta0/0mYkA36MyRmQZU0KnCA9LuOLLOnHFF5GVcXIC3N3ljkK/vx/9u9CO9InE6gmrTfb6G5PFVV87cnfY/elaRfVFWn1MfBGRuSgUgJfX0OPq2+uRU5sj9M0In2GmqOQlXfGVW5vrMFvtpau9lAol4gLiZIqGiGxVSnmK0J4R4xgrRyeHTBbaqZVc8WWNmPgiIoPVt9fjk9RPhL7bZtwGZ6VpFo+qVMBVk66Ci7L/KOAuVRd25e8yyetbK2l9r1CvUJupoUNE9utE2Qmh7erkiqSQJJmiMQ2VSne/dMVXt6obeXV5FohIftL6XrGjYuHqZMUn7BCRVUqpSBHatnIQykhNCZ0itE+VpUKlUusZTXJh4ouIDLY1fSvautv62s5KZ9w8/eZhv96ePcCDDwIXXQSMHg089hjg6+aLxdGLhXH2XueL9b2IyBpJtzlOCZ0CN2c3maIZnoIC4NlngfXrgcREYPZs3eP8PfwR6hUq9GVWOcZ2R+mJjixsT0TG6lZ1a23xc5TE14RRk4FDvwO+fht45xDqnziLE9nlcodFEqzxRUQG+0/af4T2mglrEO5jwLFgenz3HfDCC/3t1F9XBl887mLsLtjd1/9tzrdQq9VQGHo8l42RJr5i/e27ECgR2QZpYXtb3OZYXAw8+mh/29VVU0fTxUV7bEJwglDrylEK3GfXsrA9EY1Mdk022rvbhb6poVNlisay4oKigT3PAB2+fX3f7c/AzInDv0ci0+OKLyIZqdVAZ6fcURimtLEUPxX+JPRdO/naEb3mZHFLPE7/+qDoonEXCf1lzWVadQPsiXSrY4xfjDyBEJFd6ugY3nX2UNheOs90dgLZ2brHTgx0zAL30hVfLGxPRMY6evY0MGB3X6RPJIK9guULyIKcnZTwHi3W6z14vEWmaEgfJr6IZFRYCHh7a46Vv+EG4NVXrfdEx63pW6EeMKP5uvlqJaiMNUXcEo+iIqChQfNLd5y/WFjXnrc7cqsjEZnT9OnA2LHA5ZcDTz2l2f43lLq2Oq0aV7a44svfX7OVfqBUPXWHE4LFAveOsOJLpVZpFbfnii8iMtYbz4cBf6kCPtwJfP8Comqvlzski4oaVye0M9N1LCsmWTHxRSSjU6c0ia5Tp4APP9TckDhb6Qbkf6f9W2hfnnA53J1HdvzkxImaUywHSksDFAoFLh53sdDvSIkvbnUkIlNpawPOnNEku778EnjiCaCiYujrpIXt3ZzcMClkkpmiNC99q4ulpAXuz1SfgVpt3wWKSxtLhdqdAGt8EZHx8s94A21BQMFy4OD9cCpcIXdIFiWdZ8rzguQJhPRi4otIRtJfvqdO1Rwzb21yanJw7JxY6+WapGtG/Lpubprk10C9T+Klq8kOlxxGdWv1iN/T2jR2NKK2rVbo44ovIjKVjAzxJEOFAkgy4GBG6Wf+1LCpNnvSn/SGRO+KryBxxVdTZxPONZ0zU1TWQXqio7erN8K9WZeGiIxTVxgltGdPt62DUEZq0Sw/od1eHouWdhupZ+MgmPgikpH0l2/p1j9r8Wnap0I7xCsE58eeb5LX1vckfknMEni6ePb1q6HGjtwdJnlPayJd7QUAY/zGWD4QIrJL0nkmLk6zxX4o9lDYvpd0btW34mu072h4uXgJffZe50tXfS97PUiGiMwjs7AaqibxVNwL5ofqGW2fLlkYI3b0uOL7Q4VyhEJ6MPFFJCPpL9/SJJA1UKvVWtsc1yWug7PSNHsypTckvTdp7s7uWD52ufA9e9zuKE18RfhEjHgLKRFRr+HOM/ZQ2L6X9Gc+e1ZTT1JKoVBobXfMrLLvxJe0vhcL2xORsb7dXyx2OLdh2YxoeYKRSUy4H5xGlQp9uw9VyRQN6cLEF5FMWluBnByxzxpXfJ2qOKVV4PeaySPf5thLV+Krt6SKtM7Xjtwd6FZ1m+y9rQEL2xOROUkTX4bMMzWtNVqnzdpy4mviRO36mSxwryHd6sjC9kRkrF+ONgptz4gCuLo46RltvwJjxMTXiVPc6mhNmPgikklGRn+CB9DUXZlkhXWDP8v4TGhH+0Vj3uh5Jnt96ZP4hgag+NcHR9I6X/Xt9ThVfspk720NCurEm0smvojIlIazpV5a2N7d2R2JwYkmjMqyXF3115OUkiZ+smuzdQ+0EzzRkYhGKj1NTClEjquRKRJ5jZ3YKrTzzhhQV4AshokvIplIn8LHxwOenrrHyum/Wf8V2lclXmXS+h9RUYCfWA+y789mtO9orW0X+4r2mey9rUF+fb7QHjtqrEyREJG9qagAKivFPkO2Oh4vE7c5Tg2darLt7XIxtM6XdM6RJobsSXt3u9aqY251JCJjleYGCu3EpB6ZIpHXzGTxAJjawkiZIiFdmPgikslwtp9YWn5dPtIq04S+SydeatL3UCgGP3Fr8ZjFwvd+PvuzSd9fbvl1ksSXPxNfRGQa0nnG0xMYa8BHjHTFly0Xtu9l6MmO0hVPZxvOorWrVfdgG5dTkwM11ELfhCCu+CIiw3V29aC1NFboWzDDV6Zo5HX+3GCh3VMfgYJz9fIEQ1qY+CKSifSXbmssbP/fM+Jqr2DPYMwdPdfk7zPYk/jF0WLia1/RPqjV4i/qtkqtVjPxRURmI51nJk0CnAwouyJNfE0Pn27CqOQxWD3JgeID4rX6cmpytAfaAWl9r0ifSHi7cmsOERlu9/EioNtD6LtkkWOeTr5iTgzgJNb1+uaXInmCIS1MfBHJQK22jRVf0m2Oq8evhpPS9MUqB3sSvyh6kfC9mrYauzlevrKlUmslARNfRGQqw5ln6tvrkVeXJ/TZQ+JLOs80NmpOd5TycvVClG+U0Gev2x2lhfu52ouIjLXrYIXQVvpUICEmSKZo5OXp7gL3MLF2789H6+UJhrQw8UUkg4oKoLpa7LO2xFdNa43WtsK1E9ea5b2kNyTZ2UBXl+a/o/2itW5C7KXOl3S1l6uTKyJ8ImSKhojszXAK26eUpwhtF6ULJoVY4ckrRho9WrueZHq67rHSBJB0ZZS9kP5cEwMn6hlJRKTb0ZMdQntUdIlMkViHsLHiDV5GhunqItPI2HalUiIbJX0K7+UFxMbqHiuX7TnboVKr+toezh5YPna5Wd5r0iRg0SLN/yYlaf63t36+QqHA4ujF+CT1k77xP5/9GbfPvN0ssViSNPEVMyrGLCvqiMjxdHdrJ3YM2VIv3eY4OXQyXJ1c9Yy2HQoFcPnlQGdn/zwzZ47useMDxmNX/q6+Nld8ERHplpMpbnOMndAkUyTWIXlOIworvwJC0oHgdHjMAoDFQ1xFlsDEF5EMpImvpCRAaWXrL6XbHC+MuxCeLuY5dnLUKGDfIIu4Fo1ZJCS+fir8CWq12qSnS8qB9b2IyFxycoAO8UG8QYmvk+Unhfb0MNvf5tjrvfcMG+cIK77UajWyqiUrvoK44ouIjFNdECa0p0917Ae4G2/uwlfel/W185UBdnHPYg+s7FabyDEMZ/uJJbV3t+P73O+FvrUTzLPN0RDSAvelTaVaR7DbooJ6sQ7A2FFMfBGRaUjnmYgIIMiAsivSFV/TwqeZMCrbMD5wvNDOqs6ym0NVepU3l6OpU1yZIT3RkohoMOeqm9BdEy30nTcnWM9oxzApWCwNUNtWi4qWCj2jyZK44otIBn/6E7B2rWblV2oqsGSJ3BGJdufvRktXS19bqVDikvGXyBbPxKCJCPIMQnVr/775n8/+jFh/K9sfaiSu+CIic7nwQuCHHzRzzOnTgK8Bp8u3dLZobX+zh8L2xpImgBo6GlDVWoUQrxCZIjI96d+zh7MHovyi9IwmItKW15gO3PwHoGIKUDEZqJyCVfNNf/q7LYn1j4WHswfautv6+tIr0xHmHTbIVWQJTHwRySAmRvN1+eVyR6Lb/7L/J7TnR81HsJd8T3AUCgUWjVmEL8982de3r2gfNkzdIFtMpsDEFxGZy6hRwAUXaL4MdbritFDbUalQYkqolS1JtoAxfmPg5uSGjp7+vaJZ1Vl2lfiSbt8cHzgeSgU3ghCR4TLrTgOjj2i+ACQEJWCUd4bMUclLqVAiMTgRx8uO9/WlVaZh2dhlMkZFALc6EpGEWq3GjtwdQt+a8WtkiqafdLujrZ/s2NHdgZJG8eQbJr6ISE7SbY4JQQlmq+1ozZyUTogPiBf67K3AvXTFF+t7EZGxTleIRYsd8UGJLtKTkNOr9BwhTBbFxBcRCc5Un0FRQ5HQt2rcKovG0NUFZGYCdXX9fdLEV05tDsqayiwalykVNRRBDbFmjK1v3SQi2yZNfNnzNke1GigpAQoKdH/f3gvcS38e1vciImMx8aVbUnBSf6PND0dOtMsXDPVh4ouIBNLVXpE+kVqFGs1l0yZNoX9vbyAxEfjuu/7vTQ2dCh9XH2H8oZJDFonLHKTbHAM9AuHrZkARHiIiMzlRbv+Jr//8B1iwAPD3B6KigEcf1T1ufIBY4N7eVnxJT3SUJvqIiAajVquRVpkm9CWFJOkZ7Vh8m2YDH+0AXioGnq/Hqc2vQaWyrwNSbBETX0Qk2JEnJr5Wxa+y2BG8WVmaQsydnZp2+oCVwU5KJ8yOnC2MP3ruqEXiMgfW9yIia9LR3aF1E2OPia+6OuDAAaChQdNO17MDxZ5XfLV1tWmdjMytjkRkjLLmMtS11wl9k0MmyxSNdUmKiAPyVgBNozUdHX44nlUub1DExBeRpe3fD1RVyR2Fbq1drfip8Cehb2X8Sou9/yTJwjLpDYk08XWk9IiZIzIfJr6IyFzS0oD8fEClGnps3zWVaehWdQt9yWHJpg3MCkjnmTNngO5u7XHjA8UVX3m1eVp/PrYqtzZXa6u99OclIhrMT6k5QPkUoMsNAODl4oXoUdEyR2Ud5iVFAq5NQt8Ph0plioZ6MfFFZEEtLcDChUBIiObr/POBUiv6HNxbuFc4xcpJ4WTRU0iSJCukh0p8HT13VDiBzJYw8UVE5vL73wNxcYCPDzBrFvDf/w59jbS+V3xAvF1uv5Ymvjo6gLw87XHSmlddqi6tVVK2SlrYfrTvaHi7essUDRHZok8/VQNvnQKeawFey4Tv/td4MuyvlEoFvCLFeslHTjbLFA314r9OIgvKzOz/76oq4KefgIAA+eKRktb3mh81H6PcR1ns/aU3JHl5QGtrf1ua+GrsaLTZuitMfBGRufQ+NGhtBY4dM2zllzTxNS1smhkik19QkObB00C6tjsGegYiwEOcoKV1sWwVC9sT0UhlZjhp/kPtBNRMhHdH/OAXOJiIseI20L4/L5INE19EFpQmlk9BXBzg4SFPLLp8l/ud0LbkNkcASEgQ22q1ZhtKrwifCET6RApjbHG7o1qtZuKLiMyislLzNZB0Na0uKRUpQtse63v1Gmp1cS9pQshWH7RISVd8sb4XERnrXJ74YCBxUo9MkVinhETxz+Ncvr9MkVAvJr6ILEia+DLkZsRScmtzkVubK/RZOvHl5QWMleR/7LHOV01bDZo6xb3/THwRkSlIPzPd3bU/V6V6VD1ax9Lb64ovQHt1sXRu7mWvBe654ouIRqK7R4WWc2I9r7nTffSMdkzSP4+W0mie7CgzJr6ILMiaE1/f534vtEO8QmQpbOwIBe6lq72clc4Y7TtapmiIyJ5I55mEBMBpiB0W2TXZaO1qFfrssbB9r6HmmV7jA8SC7/aw4kutVmtt2ZQm+IiIBnMgtRToFOsCrpzP32MHumBuhNjR6YODaVZU2NkBMfFFZEHWnPjakSfW91oZv1KWIpVDPYmXJr5SylPQ0d0BWyJNfEX7RcNZ6SxTNERkT4Yzz6SUpwjtcO9whHqHmi4oKyOdZ7KygM5O7XHSkw7tIfFV1lymteKYWx2JyBg7D5YJbYVnLabEhegZ7Zimjw8D3BuEvp2HyvSMJktg4ovIQurqtE9wtJbEV1dPF/YW7hX6VsStkCWWoZ7EzwifAQUUfe0uVRdOVZyyQGSmw/peRGQuw0l8nSw/KbSnhdvvNkdAe57p7gZycrTHSRNfpU2laO607ZO5MqsyhbaniydXHBORUY6kiCuEfaPOQqlU6BntmJRKBXxGS052TGmRKRoCmPgishhpAsfFBRg3Tp5YpI6UHtH6ZX5Z7DJZYpHepBUWAi0D5gk/dz+tp9O2tt2RiS8iMge12kSJLzuu7wUA/v5AhGQXSkaG9rj4AO1TyqS1MG1NZrWY+JoYNFGW1d1EZLuyMl2EdlR8g56Rji0yrl5oZ5/h7g45caYjshDpzcjEiZrklzXYXbBbaE8JnSLbNpcJEwCF5KHRGfEAKq3tjkfPHTVzVKaVV5cntJn4IiJTKCkBGhvFvqESX2q1GifLHCvxBQCJiWJbV+LLw8UDY/zGCH22vt2RJzoS0UhV5AcJ7aRJXO2lS0KCWMy+vIAnO8qJiS8iC7Hm+l678ncJ7eWxy2WKBPDw0D6BTHpDYusF7nNqxD01ulYVEBEZSzrP+PgAUVGDX1PaVIqathqhz54L2/cyJPEF2F+dL+mKr4SgBJkiISJb1NrehfbyGKFvwUw/eYKxcrOTxQMAWs6N4cmOMmLii8hCrDXx1dzZjIMlB4W+ZWPl2ebYa6gbEmni60z1GTS028Yy69auVpQ2icXexgVYyZ5XIrJpuuYZ6QpaKelqL183X8T6x5o4MusjnWcyM3WPs7eTHaU1vpj4IiJj/Hj8LNDjJvStmj9Gz2jHtnyO9smORzLPyRMMMfFFZAm66q5Ii+vKZV/RPnSruvvazkpnLI5eLGNE2jck0q2OU0KnwNXJVeizle2OebV5Wn1xAXEyREJE9kZaS3I49b2Sw5IdouaTdJ7JyQF6erTH2dOKr4b2BpQ1i6eKJQQz8UVEhvvxcKXQVvqWIy6SW/h0mT4+DHATH8z/eJSJL7mwwhqRBVRWAjXiThKrWfEl3eY4b/Q8eLt66xltGZdcAowapbkxSUgAYiWLD1ydXJEclixscTxRdgLLx8q3RdNQ0sLIkT6R8HTxlCkaIrInLGxvuKQk4I9/1MwxiYma+pJOTtrjpImvrJosqNVqKIZaSmeFpNscnRRO3GpPREY5fqpDaPuPKQUQJk8wVk6pVGDMmg9xti0dCM4AgjLhPuERALPkDs0hMfFFZAHSmxEPD+1kjlykhe2tIXm0cKHmazAzwmdoJb5sQU4t63sRken19GhvCzck8ZVSniK0HSXx5e8PPPPM0OOkia/69nrUtNUgyDNIzxXWS7rNMT4gXmv1NBHRYHLPuAvt6HHNekYSACy/9hTeS3mvr51RpaegJJmd/a9lJ7ICBQVie9IkQGkF/++raK7A6YrTQp81JL4MIb05s5XEl3TFF+t7EZEplJYC3d1i31CJr7q2OhTWFwp9jlDY3hjRo6LhohSPYLbV7Y7SEx25zZGIjFVTJu4KmTpZx1JZ6iP9nJWuvCXL4YovIgu4+WZg/XrN0/i0NM2KL2vwY8GPQtvH1QezImxj+e308OlCO6c2B40djfB185UpIsNwxRcRmcOYMUBLi6ZWVVqa5n9DQga/Rrray9XJFYnBiboHOyhnpTPiAuKEpFF2TTbmR82XMarh4YmORDQS7d3t6LxpKtAQAVQmAZVJuPiC6+QOy6pJ59SMqgyb3S5v65j4IrIQHx9gzhzNl7WQ1vdaGrMULk4uekZbl6SQJDgrnYXC/KfKT2FR9CIZoxqa1oqvQK74IiLTcHHR1KuSFm7XR1rfKykkyWbmAEsaHzheK/Fli6SJr4lBE2WKhIhsUVZ1FtRQAX4lmq9xO7Bi9hNyh2XVpImv+vZ6lDeXI9wnXKaIHJcVbLYiIrlYY30vQ7k5u2FSsHg0prVvd2ztakVJY4nQxxVfRCQXR63vZazxAbZ/smN7dzvy6/KFPq74IiJjpFeJRwdH+0XLfiCWtRvjN0brECvW+ZLHsBJfb7zxBmJiYuDu7o45c+bgyJEjg46vr6/HXXfdhfDwcLi5uWH8+PH49ttvhxUwEZlGQV0BihqKhL7zY8+XKRrd1Grg3Dlg1y7gww+1vy/d7nii3LoTX9KbDgCI84+TIRLrx3mGyPykiS9Hre/V0AAcOgS8957mFGYpaYF7W0x85dTkQKVWCX2OvuKL8wyRcdIrxcTXpJBJekZSL6VCqXnI0O0CVCYC6Vfi2z01coflkIze6rhlyxbce++9eOuttzBnzhy88sorWLFiBbKyshCio5hEZ2cnLrjgAoSEhOCzzz5DZGQkioqKMGrUKFPET0TDtKdwj9AO9gzWWkElp9RUYNEizQ0JoDlm/pprANcBB1BND5+O91Pe72ufLDsJa5ZTI9b3ivCJgJerl0zRWC/OM0Tm197drrX1zRFXfE2Zoplvev3vf8All4hjpImvnFpNEkmpsJ2NE9K/69G+o+Hj5iNTNPLjPENkPOmKL2u6b7Bmbd/9CfjvCkCtSb3saN6Hl26SOSgHZHTi6+WXX8Ytt9yCjRs3AgDeeustbN++He+99x4eeughrfHvvfceamtrceDAAbi4aOpGxMTEjCxqIhoxaeJracxSqyq0GBXVn/QCgJ4eTbHmSQPmWOlNWkZVBtq62uDhYiWnB0hI63txm6NunGeIzC+9Ml2okQgAU0KnyBSNfHwl56FkZAyd+GrvbkdJYwnG+I0xc3Smo3Wio4Nvc+Q8Q2Q8Jr6GJyrUCxnq/rRLab51H8Rlr4x6VNXZ2Ynjx49j+fL+OkBKpRLLly/HwYMHdV7z9ddfY968ebjrrrsQGhqKpKQkPPfcc+jp6dH7Ph0dHWhsbBS+iGzV558DH30EnDgBtLXJHY2GWq3GngIx8XVezHkyRaPbqFFARITYlyk5AXhq2FQo0J+s61H3ILUyFdZKeqLjuAAWtpfiPENkvJQU4O9/B/btA6qrDbxGss0xPiDeIVcASQ8CyNBReiXMO0yrjo2tbXfkiY79OM8QGa+2sQ25X18OZF8E1EUDKgW3OhpoxlTxgXxjyWiZInFsRiW+qqur0dPTg9DQUKE/NDQU5eXlOq/Jz8/HZ599hp6eHnz77bd47LHH8NJLL+GZZ57R+z6bN2+Gn59f31dUVJQxYRJZlRdeADZsAGbMALy8gHfekTsizcqj0qZSoe+8WOtKfAFD35B4u3prPYm35u2OXPE1NM4zRMb73/+AO+8EliwBgoOByy8f+hrW99IwJPGlUChsvs5XZpUk8RXsuIkvzjNExvv+UBGw63ng39uBVwuBP9cj1ttxP0eMcf6sMKGtbglCZqGBT6nIZMxenEClUiEkJARvv/02ZsyYgfXr1+OPf/wj3nrrLb3XPPzww2hoaOj7Ki4uNneYRGahVou/RKvVmi18cpNucwzzDsOEwAkyRaOfITckWgXurfhkR674Mg/OM+To0sXdJ4iMHPqalIoUoZ0cmmyyeGyJdJ7JzNTM1VK2nPjqUfUgqyZL6HPkFV/DwXmGHN2+Y2JBdmfvBgT6sk6tIRZNHQ04twt9Pxwq0TOazMWoGl9BQUFwcnJCRUWF0F9RUYGwsDCd14SHh8PFxQVOTk59fQkJCSgvL0dnZydcB1aq/pWbmxvc3NyMCY3IKhUXA01NYt8kK1gVLE18nRdznlXV9+plaOLrP2n/6Wtb68mObV1tKGkUJzmu+NLGeYbIeNLE11DzjEqtwqnyU0IfV3xpNDcDJSXaD6nGB9hu4quooQjt3eJNlyOf6Mh5hsh4KaldQjsguhyAFTzNtwGuLk5wD8tBe0n/5+6hlAb84WoZg3JARq34cnV1xYwZM7B79+6+PpVKhd27d2PevHk6r1mwYAFyc3OhUvUfoZydnY3w8HCdkwSRPZHejPj6AqNl3tZtC/W9eklvSLKygG6xFrNWgfvUilR09YiTszXIq8vT6mPiSxvnGSLjdHVpPhsHGirxVVBXgKZO8amMoya+IiMBH0lpM10PWaQrvqQrqKyZdJujv7s/Qry0Ty50FJxniIxXkC3WqYoZ1yJTJLYpJLpWaGdk6hlIZmP0Vsd7770X77zzDj788ENkZmbijjvuQEtLS9+pKBs2bMDDDz/cN/6OO+5AbW0t/vCHPyA7Oxvbt2/Hc889h7vuust0PwWRlZL+8pyYCMi9sOpM9RlUtIhPOa2xvhegnfjq7ATy88W+aeFi4qujp0OriK81kNb3CvcOh5crl4jrwnmGyHB5eZrk10BDJb6k9b2CPYMR4ROhe7CdUyi05xrpQysAmBAklgMorC9ER3eHGSMznbTKNKE9KWSSVa7ytiTOM0TGqTkrroacOtmojWMOL35Cp9AuznW8w2TkZvS/2PXr16OqqgqPP/44ysvLkZycjB07dvQViDx79iyUyv58WlRUFL7//nts2rQJU6ZMQWRkJP7whz/gwQcfNN1PQWSljN1+YgnSbY6jfUcjzj9OpmgGFxioKdRcVdXfl5kJjB/w4D3AIwAxo2JQWF/Y13ei7ASmhE6xXKAGyKmR1PcKZH0vfTjPEBlOOs+EhQEBAYNfc7JcPAQkOSzZoRMhiYnA4cP9bekJwoD2ii+VWoW8ujwkBidqD7Yy6VXiP5JJwVbwy4jMOM8QGa66oRXd1eK2xsUzA2WKxjZNn+yGHwe0G0oc82GTnIaVqr377rtx99136/ze3r17tfrmzZuHQ4cODeetiGyaLSS+rLW+V6/EROCnn/rbmZnA2rXimGlh04TE18myk7gh+QaLxGcorRMd/bnNcTCcZ4gMI51npKuXdOGJjqIESZ13XYkvXzdfhHmHoby5/9S/rOosJr5sGOcZIsN8f6gIwMAPShVWzouWKxybtGhmMF4c0FY1hqGooh7RoaPkCsnhmP1URyJHJT3REZA/8aVWq7G3cK/QtzRmqSyxGMqQGxJpnS/paWXWQOtER674IiITGM4DFia+RNJ5JiND98mO0tOPbaHOl0qt0qrxNSmEiS8iMtzP0hMdA4sR5OcpUzS26fwZYwClWKj4h0M86dWSmPgiMpOzZzWnQw0kd+IroyoD1a3VQp+1FrbvpeuGREp605ZSngK1rrsWGUlPAGNheyIyBWMTX1UtVShtKhX6HD3xJV0lV1cHVFZqj5MmvmzhZMeCugK0dbcJfVzxRUTGSEkVEzaB0eV6RpI+3h6ucA0uEvoOnKiXJxgHxcQXkZlIb0b8/IAImbdzS1d7jfEbg1j/WHmCMZCuFV/SnJb0pq2xo1HY+ii3po4mrRtNRz5KnohMo6sLyJbkXoZKfJ2qOCW03Z3dtepXOZroaMDdXezTtbpYWuDeFlZ8Sbc5BnoEOvSJjkRkvIJscXVX7Pg2PSNpMIFjqoR2WkaPTJE4Jia+iMxE11N4uUtp/VT0k9BeEr1EpkgMJ30S39ICFEtWBo/2HY1AD7HIprR4s5zOVJ8R2kqFEuMCuNWRiEYmJ2fkJzpODpkMZ6Vjn87l5ARMEHNaOlcXSxOEWdU2kPiqlNT34omORGSkWsmJjsmTXWSKxLbFjmsX2kW5PN3dkpj4IjKT4RQcNie1Wm2Tia+ICMDn1xN/FQogJkY85VHTr9C53dFaSBNfY/3Hws3ZTaZoiMheSOeZ8HDA33/wa1jfS7eBc3RQENDRoT1GutWxpq0GNa012gOtCAvbE9FIVNa1oLtmjNC3aAZPdByOqUkDEobOrWjprpctFkfk2I/4iMzI2grbZ9VkobJFLFpi7YXtAU2ya8sWICxM80TeU08tzeSwZOwu2N3XtubEF7c5EpEpSOcZnug4fPffD9xxh2Z7fVCQ7jGx/rFwUbqgS9W/zC6rJgvzPedbKErjMfFFRCPxw+GzkJ7oeOHcMfqG0yAuvcgHf0+7CAjOAPzOok0JtHY1w9OFBwVYAld8EZmBNZ7o+FOhuNor0icSY/3HyhSNcVatAqZN05/0AnQXuLcWmdVisZiJgUx8EdHIGTvPtHW1aSXip4ZONXFUtmn6dGDRIv1JLwBwVjojLiBO6LPmAvc9qh6tv2+e6EhExtgnPdExiCc6DtfCxHgoxu8A/IsApRpqqG1iy7y9YOKLyAwaGoCpUzUF7XvJvdVxb9Feob0kZold1fmYFjZNaBc3FlvNFhTpjUdCcIKekUREhgsIACIj+9tDJb7Sq9LRo+4vpquAAlPDmPgyhnS7ozXftOTX5aO9W6wpwxVfRGSMc02lQFAmoNCc7BjEEx2HzdPFE9GjooU+6cNxMh8mvojMYNQoYP9+zZHopaXAzp3ynuioVqu1VnzZQn0vY0wImgA3J7FuljWs+urq6UJuba7Qx62ORGQKf/87UFIC1NcDBw4Aa9YMPl76mTgucBy8Xb3NFp890ipwb8UnO0q3OQZ7BiPYK1imaIjIFimSPwbuTgT+6AXckYSLbjsod0g2LSFIfPidUaXjJBUyCya+iMxIodAkvJYvl/dEx9zaXJQ1lwl99pb4clY6Y3LoZKHPGhJf+XX5Qj0YgIkvIjItPz9g3jxNLcTBsL7XyGmt+LLmxJeOEx2JiIzRl5hx7gRC03HenEH2g9OQEoPFLUBc8WU5THwROQDpaY5h3mFaT63tQXJostBOqUiRJY6BpNscQ7xCEOARIFM0ROTItBJfks9MGtqEIDHxlVubix5Vj57R8mJheyIaidauVhTUFQh90sQNGUe64iuziokvS+GpjkQOQJr4WhJte/W9GhqAzMz+r6VLgYsuEsdMC58GnOxvW8OKL57oSETWQKVW4VTFKaGPK75EnZ1Adnb/POPuDjzwgDhGuuKrs6cTRQ1FVnlYDBNfRDQSZ6rPQA11X1sBBX+PHaEJgQlA/RigKhGoSsCZmkS03tQFT3cXuUOze0x8Edk5tVqNvYV7hT5b3OZ4++3Ap5/2t9vatBNf0pu4zKpMtHW1wcPFw/wB6sETHYnIGuTX5aO5s1noY+JLtG0bcN11/e3YWO3EV5BnEPzd/VHXXtfXl1WdZXWJr25VN090JKIRkW6XjvWPhacLT3QciUiXROCVor62GsCPx/NwyYI4/ReRSXCrI5GdK6wvREljidC3JMb2El/SUzEzdawMnhwyGQr0r2TrUfdoPfG2NJ7oSETWQLoCNsQrBGHeQxQFczAJko/nwkLNQ5aBFAqFTRS4z6vNQ2dPp9DHFV9EZAyuGjW92IhRUPpUCH37jlfJFI1jYeKLyMQOHACuvx7YvBn4+mugoGDoa8xJus0x2DNYa3+5LZDekOhKfPm4+SA+IF7ok3O7o1qt5lZHIjK5ri7giiuAP/4R+OQTICUF6O4e/Bpdhe1tbcu7uU0QdzFCrQaydOS0pHW+sqqtL/ElvWEN9QpFoGegTNEQkS364s1k4NtXgWO3AkULEec5Te6Q7ILv6HNC+8TpdpkicSzc6khkYgcPAh9/3N+eN0+TDJPLvqJ9Qntx9GKbvNmRJr7OndPU/fLzE/uTw5KRU5vT1z5ZdhJyKW8uR0NHg9DHxBcRjVReHvDFF2JfdTUQOEheg4Xth+blBcTEaFZ69crMBJKTxXG2cLLj6YrTQpvbHInIWAU/zwOqo/vabXG/AGtlDMhORMU1on7AA/zcbNb3sgSu+CIysXTJzjrpFj1Lk674Why9WKZIRmbcOMDJSezTteprWpj4NOpkuXyJL+lqLw9nD4zxGyNTNERkL6TzTGjo4EkvQPeKL9ImfciSkaE9xhYTX1NCpsgUCRHZouqGVnRXRwl9C2dw1agpTJQ8A68s5J+rJTDxRWRi0l+SJ8n4kLWksQT5dflCny0WtgcAV1cgXtzFqDvxFS4mvk5VnJLtqHlp4mtC0AQoFfzYJaKRMXaeqWqpQmlTqdDHxJduhtSTlG51PNd0Do0djWaMynjSxNfUsKkyRUJEtuiHw0UQUwUqrJwXrW84GWHuNF+h3VYWjc4uee5VHAnvwIhMSK3WviGRc8XXz0U/C+1R7qOQFJIkUzQjJ/2z1PUkXrriq7WrFdk12WaMSj+tEx25zZGITMDYeeZUxSmh7eHsoVWgnTQMmWfGBYzTeoiRWaUjQyaT5s5m5NXlCX1TQrnii4gM9/OxWqHtHFiCID+e6GgKF84dLXZ0e+BAWqnuwWQyTHwRmVBxMdDUJPbJmfiSbnNcNGYRnJROekZbP0OexId6hyLCJ0Lok2u7o9aJjjZ4qAARWR/pVsehVnxJtzlODp1s03OBOUm3OubkAJ3i4Yhwc3bTOkhF+qBDTqkVqULbSeGExGCZ6y4QkU1JSe0S2gFjymWKxP4kxgRB4VEn9O09WqFnNJkKE19EJiR9MuzjA4werXusJegqbG/LDKm9AgDTw6cL7RNlJ8wU0eB4oiMRmVp3t/ZJg0M9YGFhe8NJ55nubiA3V8c4yYOMjCo9E5IMpNscJwRNgLuzu0zREJEtKszxENqx41plisT+KJUKeEcWC31HT7XIFI3jYOKLyIR0FbaX6wDFypZKrSfQtp74kt7cFRYCLTrmCWsocN/Q3oDiRnFSY+KLiEYqL097BZKxK75Y30u/UaOACHHRsM7VxdIVVNa04kursD23ORKRkaqLwoT2lMnOMkVinyLH1gvt7DNchW1uTHwRmZA1FbaX1vfycvHSWgllayZMEBOJarX2ygdAe8XXybKTUKvVZo5OlFopbjVxVjoz8UVEIyadZ4Y60bGtq01r9SkLnQ/OkNXF1rziS1rTbWoo/76JyHC1jW1aJzounsmTB01pwkTxvqSswF+mSBwHE19EJqRrxZdcpNscF4xZAGelbT+t8fQEYmPFPp0nO0pWfNW116GoociMkWk7VS7eeCQEJcDVydWiMRCR/TF2nkmrTEOPuv+0KAUUXAE0BEMK3EtXfBXUFaCtq82MURlGrVZzxRcRjcj3h6QnOgIXzhkjTzB2alayl9BuOTcGKpVlH9I7Gia+iEzE2k503HdWUt9rjG1vc+xlyJP4MX5jEOARIPSdLLPsdkceJU9E5mBs4ku61Xt84Hh4u3qbOCr7YkjiS7qCVw01smp0LEG2sKKGIjR1iqfsMPFFRMbYd6xGaDsHFCPE30vPaBqO5bMle+o7fHE8iwcImBMTX0QmUlKifaKjXFsd69rqtFYcLYlZIk8wJmbIDYlCodBa9WXpAvenKyVP3EN440FEI2fslnrW9zKe9AFLVhbQ0yP2ebl6IdovWujLrJK/zpd07g/wCECkT6RM0RCRLTqVJj3RsUymSOzXrIRwwFW8cdx1+JxM0TgGJr6ITET6FN7HB4iK0j3W3PYX74ca/ctl3Z3dMStiljzBmFhv4is4GFi8GJg2Tfc4rTpfFixwr1KrtI6T5xN3Ihqp7m7gjFiuy+jEl/ShAGnrnWc8PIDp04ErrwSam7XHJQRbX50vXdscFXKdskNENqkgWzzRMWY8T3Q0NaVSAa+Is4CiBwjMAiZ8hfKOPLnDsmu2XfCHyIpY04mO0vpecyLnwM3ZTZ5gTOyKK4CLL9YkvgYj54qvgroCtHSJx00y8UVEI2XsiY49qh6tQudc8TW04GCgoAAYMwZQDvKIOCEoATtyd/S1reFkR+lqYxa2JyJjSU90nMoTHc1i1aPv4LPCvwPOmom9PeJWAOvkDcqOccUXkYlY04mO0sTXkmj72OYIaFbSDZX0ArRXfJU1l6G82TJ756VP3IM8gxDmHaZnNBGRYaTzTEjI4Cc65tbmorVLfFLPxJdhYmIGT3oB2gXurWHFl3SrIx+6EJExdJ3ouHBGgJ7RNBKzJkb0Jb0AIKNa/jnEnjHxRWQiCxcC69ZpEl4uLvIlvpo7m3Hs3DGhb3G0fRS2N8a4wHHwchELcVqqwL10hQW3mhCRKUREADfdBMydC/j6Dj3PSLd4h3uHI9Q71IwROpaEIHGrY05tDrp6uvSMNr+Wzhbk1uYKfUx8EZExTpfkAHP+Boz9AfApBaDCyrnRQ15HxpPOIRlVGVCrebKjuXDdIpGJbNyo+QKAri7NlxwOFh8Ujq53Vjpj7ui58gQjI6VCieSwZOwv3t/Xd7L8JFaNW2X299aqscLC9kRkAnPmaL4AzUnC0gNVpFjY3rykNb66Vd3Irc3V6reU9Kp0ob6nUqHEpGAZl58Tkc0p6T4NrNrU1452n4wQ/9ODXEHDJV01XNtWi6rWKoR4hcgUkX3jii8iM3BxATw95Xlv6TbHWRGz4OXqmEcQy1XnS5r4mhrGGitEZFoKhWbV12CkK75Y2N60AjwCEOolrqCTs86XdJvj+MDx8HDx0DOaiEhbeqVYtHjyGK72MpeYUTFwd3YX+qxhy7y9YuKLyM7sOysmvhxxm2MvaZ0v6RZQc2jubEZenXgqC7eaEJGlqdVqre3dXPFletZU50u6wo9zDxEZS1pnKjEoUc9IGiknpRMmBE4Q+jKr5D8kxZJ25e9CTWuNRd6LWx2J7Eh7dzsOlxwW+uwx8dXSAnz7rabQc2YmkJMDHD4MOEs+0WZFzhLaRQ1FqGiuMGuNm7TKNKGtVCi1boyIiMytrLkMVa1VQh8TX8bZvRtISdHMMxkZwLPPAuedJ45JCErAnsI9fW05V3wdKxMf7kwPm65nJBGRbtIVX5NCuF3anEZ3LsepY7OBqkSgKhEf5HTjjo/ljsoyGtobsOLjFVCr1ZgWPg3LYpfhoYUPIcDDPIcpMPFFZEeOlB5BR09HX1sBBRZELZAxIvNob9ccJDBQfj4wfrzYlxCUAG9XbzR3Nvf1HSk9gtUTVpstNulWkwmBE7SWMY+EWg3s2gXMnw94OeYOViIygHT1j7erN+IC4uQJxkY9+ihw6FB/OyVFO/FlLSu+unq6tOafGREzhv16hw8DY8YA4eEjjYyIbEVrVyvy6/KFPtYJNK/205cA3yzta+e4H5cvGAv7z74jUJUnAiFpOFF2AumV6fjT0j+Z7f241ZHIjkjreyWHJcPP3U+maMwnMBAIkdR9TE/XHuekdMLMiJlC35HSI2aMTEdhexNvNTlzBrjwQsDfH1iyBHjqKaCzc+jriMixSBNfU0OnQqngr33GSJDUqNc1z0gL2Z+pPoMeVY/2QDPLqMoQHnwB2tv9jXHNNZpTRCdNAn7/eyA3d+hriMi2nak+IxyQAQATgybKFI1jmD5FfDjeUDxapkgs7+9vuAJ/TwVeLAM+/xgTG/5g1rqU/A2IaITUaiAuTpOEuPNO4I03gNpaeWKRJr6WRC+RJxALkN6QZOh5yD47YrbQPlx6WPdAEzldad7E165dmv/t6gL27QPeeUdzmAIR2bebbgJmzAA2bACefx5ITR18PAvbj1yiZJe6rnlGuuKrvbsdBfUFZoxKN2kNy7H+Y4e9XSQ/Hyj49UfIyABee01TYoCI7Nu7/2oF/pYFfPoFsPtphJbe6LAHZFnK4hnBQlvVFIqCc/XyBGNhZ478muRrCQNSr0VIwyqzvh+3OhKNUEmJ5pfE/HxNIgIA1qwBAsyzPVmvrp4uHCg+IPTZY32vXpMmAT/91N/W9SQeAGZHiomvo+eOQqVWmWXlg1qtNvuKr927xfby5ZrT3YjIvh09qkl2nfj1cFo/P2DyZP3jpSu+WN/LeJMkO3zS0zUPuwZ+5oZ6hSLIMwjVrdV9fafKTyE+IN5CUWocLxO3x8wIH/42R+k8Exw8+L81IrIPJ1K6gdrxmq8zl0HdbN5dEgScP2MM4NQJ9Lj29X138CzuvGKUfEFZwInscnSWi+UXrlkTrGe0aXDFF9EISRMuPj7AaBlWqZ4oO4GWLvGR7MIxCy0fiIXouiHRZc7oOUK7vr0eOTU5ZompoL4AjR2NQt/U0Kkme/3ubmDPHrFv2TKTvTwRWanubiArS+yTfgYO1NTRhNxacW/atHCu+DKW9M+4sREoLRX7FAqF1gOOUxVirS1LkK74MmXia9kyQMk7BiK7V5AtbjOLHd8qUySOw9PdBW6hRULf/mP18gRjQW9vk+yfd2/AtSvMu62W0xjRCEm3PiQmyrMCR7rNMTE4EcFe5s2cy0l6Q3LmjObmUCrSJxLh3mJ1XnPV+TpUckhoB3kGYbSv6bKgx49rbrwGOv98k708EVmpvDztWn7SbXgDSVd7OSudebrsMERFaR5mDZSWpj1O+oBDuvLX3Lp6urTeU1rf0lAqle7EFxHZv5qzYUJ7ShI3h1lCSIx4AnNqmlrPSPuxSzLPhE/OhKuLk1nfk4kvohGSrjQa7Cm8Of1U9JPQXjzGfrc5Atp/zp2duovvKhQKrVVf5kp8HS4R64fNHT0XChNmQXvre/VKTNQUHyYi+yadZ0JDNYd86COt75UYnGjS02UdhUKhnWDUtbpYmviy9Iqv9Kp0kxW2T00FqqvFvuXLhxsZEdmK6oZWdFdHCX2LZw4y0ZDJjJsofn4X5/nKFIllqFRqFJwQtzkuXNKhZ7TpMPFFNELWkPjqUfXgl7O/CH1LYuy3sD0ABAVpbv4G0lvny0IF7g+Viiu+5kbONenr33YbsGULcMstwNixfApP5CiMnWdOlJ0Q2ixsP3yGbKufGiYmvgrrC9HQ3mDGqES6Ctv7e/gP67UmTtSs+Hr4YWDWLGDcOCAmxgRBEpFV+/5QEcTUgAor50XLFY5DmZksbjG195Mds2qyoLpmJXDhvUD8t4BLM264PGroC0eIiS+iEVCrdW91tLTTFafR0CH+km3Phe17DbfOV0p5Cjq6Tftkob27HSfLxFUW0vcdqaAgYN064O23NVufXnzRpC9PRFZK+tk21DwjXfE13NU/ZNg8kxCUAGeluCXIktsdj58TC9sPd5sjALi5abbQP/cccOSI7q2dRGR/fj5WI7SdA0sQ5OcpUzSO5fzZ4pN8dXMwss7W6Blt+34s2A2EnQbm/xW47mJEPDMZK+fEmv19mfgiGoHiYqCpSeyTY8WXdJtjfEA8Inzsfw+coYmvGeEzoED/lsMuVZdWDZyRSilPQZeqq6+tgAKzImaZ9D2kXF2HHkNEts+YFV8d3R3IqBKfyHDF1/BJ/6wzMjQPvQZyc3ZDQlCC0GfJ7Y6mPNFRivMMkWNISRUL5QaMKZcpEsdz3vQxgHO70LfjQLFM0ZjfrgKxdsvy8YuhVJq/QDYTX0QjIH0S6usrz4mO0sL2S6Lte5tjL0MTX37ufpgYJJ4UYuo6X9LC9onBifBz9zPpexCR4+nq0j7RMSlJ//i0yjR0q8QbGOlWPDKcdJ5pbgbOntUeJ/0zttSKr86eTq0kmykTX0TkGAqyxdVdYyfwREdLcXVxgnuYeLLjgRONekbbth5VD/YUiEfUL4+1TCFJJr6IRkDXU3hLn+ioUqu0El+OsM0R0L4hyc7W3CTqIt12aOo6X9LE15xI025zJCLHlJOj/bk22IovaX2vcQHj4Otm34VyzSkyUvNQayBdD1mmhEwR2pZa8ZVemY7OHvHIT25tJSJj1RaJJ6AnT3aRKRLHFBorniqi72G+rTtedlyrPM+ysZYpWszEF9EISFd8DfYU3lwyqzJR0ybuA3fUFV9dXZqbRF3MXeBe+npzR5u2sD0ROSbpPBMRAfgPUrdcWt9rWji3OY6EQjG8AvepFanoUfWYMTINaWH7OP+4YRe2JyLHVF7bjO5asbj40tlBMkXjmMZPFJ9wldjpyY678sVtjglBCRYrz8PEF9EIWMOJjtL6XtF+0Yge5RinsPj7A+HiAyqDC9zn1ubiXNM5k8RR3lyOwvpCoc+Uia+TJ4GvvwYa7XPVMxENwth5RivxxfpeI2ZQ4itUTHy1dbchtzbXjFFpaNX3ihjeNsemJuC99zS1S4nIsXx3QNxmB0UPVsx1jHsJazFzqniyY2NxFFQqtZ7RtuuTf6uAsmRApdkitSzWckfUOw89hIh0Uam0T3SUY8WXNPHlKNsce82cqflFfdIkzdfkybrHTQ2dCj83P2F57Z6CPbh2yrUjjuFwibjay9vVG4nBpjve8623NCc5OjkBc+cCd94J/OY3Jnt5IrJixqws7lH14FS5uMWOia+RmzIFiI/vn2cW65hmQ71DEeoVioqWir6+UxWnMCFoglljO1B8QGjPDB/eiY579gA33aT57wkTgIsvBl56aaTREZEt+PlYrdB2CT6LUd7mP2WP+i2bG4rNXuVASDoQnA51SDrKm55ChF/o0BfbiOqGVmS8839Az6OAZxUQuxvJSwIs9v5MfBENU0EB0NYm9ll6xZdarXbYwva9vv7asHFOSicsiVmCr7P6L9hTaKLEl2Sb46yIWXBSOo34dXvt3Kn5354eYP9+4LLLTPbSRGTljFnxlVWThbZucWLiVseR+93vNF9DmRo2FT/k/dDXPl1xGusmrTNbXPXt9UirFDOj86PmD+u1dg3YfZKVpdlSS0SO4XSauC07KLoCABNflrR0WhTcH/ZGe3f/6Y5natfbVeLrH19kAD2/PpxpDQYyr8SFiS0We39udSQaJunNSGAgEGrhz6ac2hyUN4vHDS+JcazElzHOizlPaO8p3KNnpHGkhe1Nuc0xP1+TZB1ouWUOPyEimbW3A7mS3XKDrfg6WSZuc4z0iUSIV4gZIiNdpNsdzV3g/lDJIajRvxXG1cl12Fsdex+w9OI8Q+Q4CrO9hXb8xHY9I8lcnJROSAhKEPrSK+2rwv2X25uFts/YDESF+Fns/bnii2iYZswA3n9fsw0lPV1z6pOlT3T8qVDc5hjuHY44/zjLBmFDpImv/Lp8FNUXjagmWo+qB0fPHRX6TJn4kt6MhITo385JRPZFoQC2bOmfZ9LSgMRBdlGzsL28poRKTnYsN2/ia//Z/UJ7ZsRMuDu7G/06JSXAmTNi3wUXjCQyIrIlisXPAeExQGUSUDUJs2cwRSCHSSGThHk8vcq+El/ph8TCzNMX1OoZaR78V000TJGRwA03yBuDtL7XkpglUFg6+2ZDJodORqBHoHAK5p7CPbgh+YZhv2ZaZRqaO8UnGHMi5+gZbTxdT+GVXKtL5BDc3IArrtB8GeJE2QmhPT1suhmiIn2kK76KG4tR21aLAA/z1DDZXywmvhZELRjW6+wSD9mCvz8wnf90iBxCfXs9qoO/BIL7+zZek6b/AjKbScFiLQN7Snyl5VehvVSseblutWVPIObtE5GNUqvV2okvB6vvZSylQomlMUuFvpFudxxYzwUAYkfFItTbNHtee3qAH38U+7j9hIh0UavVXPEls4lBE+Hq5Cr0SbefmkpXT5fWNvuFYxYO67WkD1jOP19zmAoR2b+MKvGkLmelM8YFjpMpGsemlfiqTIdabR8nO/59a7bY4daIGy423UFghmDii8hG5dflo6SxROiTJnUckUoFNDbq/750u+OPBT+OaFL5Lvc7oX1h3IXDfi2pEyeAujqxj9tPiEiXooYi1LfXC3080dF8Ghq0+1ycXLRWfUmTU6aSUp6idZDBcArbq1TaK744zxA5DukBGeMDx2sl8MkyJoWIia+6xnYU1ZbJFI1p/bBTJbRDJ2XC093FojEw8UVko/YW7hXaoV6hmBBo3mPTrdWBA8DttwMLFgCjRgHXX69/7HmxYuKrpLEEeXV5w3rfpo4m/HL2F6FvVfyqYb2WLtKn8BMnAqNHm+zliciOSLc5+rv7Y4zfGJmisT+VlcADDwArV2pOPAwNBbq7tcdJazweLDlolnik2xwnBE5AkGeQ0a+Tlqb52QZi4ovIcUgLqCeFDHKCCplVzKgYuB5+CNi6FXgtE9jchE++Pid3WCOmUqlRcEKsQb1waZue0ebDxBeRjWJ9r355ecA//qFJgDU1AadP6x+bEJSAUC9xK+KeguFtd9xdsBtdqq6+tovSBefHnj+s19KFp2wRkaGOnzsutGdEzHDYOcEcnJyAF14Avv8eKCsDOjqA7GztcfNGzxPah0oOmWWriqnqe0nnmdhYYOzY4UZFRLYmrUpc8SXdbkeWo1Qo4Zr1GyDjKqBmIqB2woFjzUNfaOV2HC5AT32E0HfD5VEWj4OJL6JhKC8HWlrke3+1Wq214mtp9FJZYrEGU8SDtFBYqHsbCgAoFAqT1fnakbtDaC+KXgQfN59hvZZUSwuwX7yv4VN4IgfS1ARUVRk+/niZJPEVPsPEETm2wEDNoTYD6XrIMi9KTHzVtNUgtzbXpLGo1WqtEx0XjDFN4ovzDJFjOX2mEVD1PyThii95jR4n1jjJTLf9swjf+6xYaCv9ynDRPMs/YWHii2gYfv97wMcHiIsD1q4Ftm+37PsX1BeguFH8EFkS47iF7SdOBJwl80LaIAfSSFdlDafOl1qt1qrvZcptjvv2AV39i8ng5AQscdy/YiKH8+WXQEiI5uv884Fnn9U/Vq1Wa5/oGM5j+UxN+pBFV+Ir2i8aYd5hQp+ptzsW1heirFms+zKcFV/t7Zq5ZiCuLCZyHFlna1C9+SiwuQl4+wjw1XuIcp0sd1gObfJk8X6kLDdYz0jb8cted6EdOz0PSqXlV6QPK/H1xhtvICYmBu7u7pgzZw6OHDli0HWffvopFAoFLr300uG8LZHVSEsD1GogPx/4+mvgnIW3X0tXewV7BiMhKMGyQVgRNzdN8mug1FT946UF7itaKnC49LBR75lZnYmzDWeFvpXxK416jcH8IB4WiTlzAD8/k7281eM8Q46uN3lfVQXs2QMcPap/bEljCapaxeVhXPFleoYkvhQKhXadr2LTJr6k2xyDPIMwPnC88a+zH2gbUGZFodAkWR0J5xpyZDsO/PoQvcsLODcLSP0NJkfFyhuUg1s8e5TQbi+PRXNbpzzBmEBzWycq0sTTGy+8QJ4yDEYnvrZs2YJ7770XTzzxBE6cOIGpU6dixYoVqJRWxpQoLCzE/fffj0WLFg07WCJr0NkJ5OSIfZMsvB1eWt9racxSh6/lMlnygGqwOl/xAfFaNwn/Sf2PUe8n3eY42ne0SesiSBNfK1aY7KWtHucZIiBdrDc86Dwj3eY4yn0UxvqzUJOpGZL4AnTU+So17cmO0kNVFkQtGNbvANJ5ZtYszZZOR8G5hhzdwZNiXRD38EK4u9r+1jpbtnpRjNjR44rvDxfKEYpJvP+/TKBTLANz19XGP6gxBaMTXy+//DJuueUWbNy4EYmJiXjrrbfg6emJ9957T+81PT09uPbaa/GnP/0JY1kxk2zcmTPaJzklJuoeaw666nstieYeOENvSADNE/lrkq4R+rZmbEWPqsfg99O1zdFUycfiYiAjQ+y78EKTvLRN4DxDpL1qddDEl6Sw/fTw6Q7/MMQcpPNMcTFQV6c9Tpr4Ol1xGs2dpitQLH34NdzC9t9/L7YdaZ4BONcQpaaK80RobLVMkVCv6DA/OPmXCH27DxpR8NPKfPpfcZL0iDqDSbHybN80KvHV2dmJ48ePY/mAAgBKpRLLly/HwYP6l3E/9dRTCAkJwU033WTQ+3R0dKCxsVH4IrIW0tpRUVHAqFGWe//C+kKtLXbSYu2OSFfia7CyXdLEV3lzuVZCUZ/mzmbsKxILo5iyvpdKBdx5JxAfr2mPGqV5Eu8IOM8QaQ7nKBbLOGqtah3oRLmkvlcY63uZw4QJgIuL2KernuSMiBlwVvavmlCpVThaOsheVSMU1RfhTPUZoW9x9GKjX0etBi67DJg7F1D+ejfgSCuLLTHXcJ4ha1eSK9bQmJjYpWckWVJgjFhD58Qp293qWOHzLTD+a8BF8/Bn6oJy2WIxKvFVXV2Nnp4ehIaGCv2hoaEoL9f9Q/zyyy9499138c477xj8Pps3b4afn1/fV1SU5Y+7JNJH+hR+sJsRc5AmZ4I8g5AYbMElZ1ZKmvhqagKKivSPnxA0AdPCpgl9/0kzbLvj97nfo7OnfxJyVjpj2dhlBsc6lOho4I03NFtq8/KATz/VFLd3BJxniLSTKc7OmqSLLmq1WmvF14wI1vcyBxcX7RXeulYXe7p4YmroVKHPVAXupdvsAz0CMTNiptGvo1AATzwBHDwIVFcD27Zpakk6CkvMNZxnyJqpVGo0FscIfXOne8kTDAniE1qEdt4Zb5kiGZnq1mrkh78I/GYt8GAAcMMS3HWr+9AXmolZT3VsamrC9ddfj3feeQdBQUEGX/fwww+joaGh76tY+tiTSEayJ76K9grtJdFLuKUFmmPm/f3FvsG2OwLaq74+z/wcHd0dQ77X60dfF9oLxyyEr5uvQXEaa+xYx3oKbyzOM2SPpPPMxImAq6vuseeazqGipULoY2F78xl2na8S09T52pEnJr4ujLsQTsqRPRnx9weuvFJ7NRv1G85cw3mGrNnRzDKgXVzxdeG8cJmioYFmThMn/NrC0TJFMjK78ndBjV+33zh3wWvccVy1aNrgF5mRUdXrgoKC4OTkhIoK8ResiooKhIWFaY3Py8tDYWEhVq9e3denUqk0b+zsjKysLMTFxWld5+bmBjc3N2NCI7IY6Q1JUpLl3lutVmNPwR6hj9scNRQKzQ3JTwNKn5w+DaxZo/+a9Unr8cCuB/ra9e31+D7ve6yZoP+i0xWntVbd3Zh843DDJgnOM0TGzTMnysRtjr5uvogL0P43T6Zh6EEq86LmCQ9JDpYchFqtHtGDqs6eTuzO3y30mfI0YUdiibmG8wxZsx0HSgFE9He4NWDupEjZ4qF+y+aG4m8D2qqGcOQU12JcVIBsMQ3HD3niCSpLY5bCzVm+z0SjVny5urpixowZ2L27f9JVqVTYvXs35s2bpzV+4sSJSE1NRUpKSt/XmjVrcN555yElJYVLfsnmNDYCZ8XyWhZd8ZVfl4/iRvGJ4Xkx51kuACsnfRIvvXmUGuM3BgvHLBT6htru+LfDfxPaoV6hWDdpncEx0uA4zxAZt7JYeqLjtLBpUCrMuqDfoemaZ37NfwikK76qW6uRV5c3ovc+UHwATZ1NQt+KOC4JHg7ONeToDp8Ut9P5RBVBqeQOEmtw4ewYwEncgbJ9/1ndg62UWq3G93niCSpyz1dGn1d677334re//S1mzpyJ2bNn45VXXkFLSws2btwIANiwYQMiIyOxefNmuLu7I0nymHLUr1XApf1EtkBad8XJSbMFxVL2FIqrvUK8QljfawBDn8QPdE3SNcLR8F9nfY26tjr4e/hrja1urcYnqZ8IfbfPvF3Wpxf2iPMMOTK1emSJL25zNC9p4qulBSgoAKQLS2NGxSDEKwSVLZV9ffuK9iE+IH7Y7y2t7zU9fDpCvUP1jKahcK4hR3YmQ0wDjI6vlycQ0uLu6gyPiBy0FSf09f18pAH3XC1jUEbKqMrAuSaxSP+FcfIeHWz0I8H169fjxRdfxOOPP47k5GSkpKRgx44dfcUhz549i7KyMpMHSmQNpDcjEyYAllzFLk18nRdzHut7DSC9IcnOBtraBr/mqsSr4KTor4/S2tWKR398VOfYd46/g/bu9r62i9IFt8+8fdjxSrW3A7W1Jns5m8V5hhzZuXNAfb3YN2jii4XtLSosDJCWeNL1kEWhUGidtrg9Z/uI3lua+FoZN7xtjvz41OBcQ46sLC9YaCclDXIUOllcRHyN0E5Lta2V3J8d/Rno6b+/ivaLxvjA8TJGNIwVXwBw99134+6779b5vb179w567QcffDCctySyCnLX9/qx4Eehj9scRf/f3n3HNXXufwD/JIywl8gGB+6B1oWIW6sVR621VWvV9lq77PjVLm17O2+r7e129do9HW2l1lGtu+69wYGA4ADBAcgmOb8/osCTAJKQ5GR83q8Xr+vzzck531O9+ZLnPKN9e+1aX9LN2q3RAElJQNc6vgc29myMMW3H4NekXytjC/cvxMN3PCzslFWuLseC/QuE947rMA4hXvprgRhr3TpgzBige3dgyBBg2DCghhkXDoF1hhyVbp3x8gKiomo+9lLBJVy6IX4x54gv87q1nuSmauX46FHgnnv0jx3ecjh+S/qtsv332b9RWlFq1CjhiwUXcST7iBAb1nKYwecpLASaNgUiIrR1ZuhQ4K67ADf5NtqSFWsNOaKiknKUXGoqxOK7+tZ8MMmifYcKnK023uFCim2t7zV3VjfgTA7QfAMQ/Td6j/WVfbCGUR1fRI5Kd6qjJdf3OnXlFLJuiFtsD2jGjq/qvLyAhx4CGjfWfjGJianfVNT/3vlfrDq9CsUV2uFhEiQ8sfoJ7J66u3K3rP/88x+czz8vvO+ZHs+YNP+//tJ21u3Zo/3Zvl38ckVE9q+mByzKWh706i5s7+XqhZaNWpopM7rlnnu0I747dtTWmdp+F0homQAFFJW7Wt0ou4Gt57YaNd1jXYq4Voqvyhc9I3oafJ5Nm4CyMiA1FfjiC+Crr4ArVxy344vIEW08cA5Qi9OuR/RpIlM2VJNB/Tzw55afgOCjQPBRqMPPQK053eBdfC0hN68IV5I7ABVuQNJ9QNJ9CB6wS+602PFFVF+Grrtiarq7OYZ5h6FlAL/g6PrmG8Pf08SvCV7v9zpmbZxVGdt/cT/m75uPp3s8jQ92fIC3/3lbeE9cRBy6h3dvaLqVJAlYs0aMDTP8YT4R2ThD6sz+i/uFNhe2t4xaBgjpCfIMQmxELHaf310ZW3V6lVEdX2vPitMcBzcfDGel4b/G69aZ+HjAx8fg0xCRDdu0+zKAqo4vpc8lRIeHypcQ6Rk3tAmePR5b2S4BkHI1Ba0DW8uXVD3NW3oCqKj2HUlZgWcmyL8mNX87IqqnrCz99Zcs2fG1KV0c+jOw2UDZh4zakxlxM9AmUBwe9uzaZ9H88+aYuXGm3vFv9n/TpNdPSgIyxQ07kZBg0ksQkQ0wZGTxvov7hHb3MNN1xpNpjGg5QmivOr0KkmTYWjpF5UX663u1MHx9L0nSjiyujnWGyPHsPyzuGOjf5IJMmVBtgr2CEeQZJMQOZx2WJxkDLV9ZJLR9Wx5HkxD5p9Ky44uonkJCtJ1f69cDH38MTJumXSfDEjSSBlvStwgxru9lWq5OrliQsEAvnn49XS82P2G+yXcm0f0yEhkJtJP/4QgRWdg//wC7dwNffgk88wzQp0/Nx0mSpNfxVX1dQrIOI1qJHV9p19OQnJts0DkSkxORX5pf2VZAgWEtDB8SfPIkcO6cGOPIYiLH4z90HvBkO2DsOKDPf9BtSIrcKVENuoR2Edq6uzhbI41GQvKupkKsZ//rsuSii1MdiepJoQCCg7U/gwdb9tonLp9AblGuEGPHl+kNaDYAk2Im4cejP9Z6zCdDP8GT3Z80+bV1O76GDdP+myMix+LlBcTGan/qcj7/PC4XXhZippx+TaYRExyDCJ8IYY3IladWol3j+j/Z+Pbwt0J7cPPBCPcJNzgX3ToTHm7ZTXqIyDqcuHoYCEoFgpKBDssw4e7v5E6JatA1tKsw2tcWOr7W70tHxZVmQuxf91nHNFqO+CKyAZvTxfW9mvg2QTP/ZrUcTQ3xxYgv8FKvlxDurf+lYs6gOfi/nv9n8msWFADbtokxTj8horrojvbyc/NDtH+0TNlQbRQKhf50xzOr6v3+c9fP6e3o/HDnh43KpaZ1JPmAhcixFJYVIvVaqhDrGGzBtVuo3nR3aT546aDBU+Ut7YvFGUJb6XMJYwe0kikbETu+iGyA7i+9A5sNlCkT25KXB2zeDGzdWv/3eLh44P0730fmc5nYN20fXuvzGqZ0moIV41fg5d4vmyXPjRuB8vKqtosLMJB/xURUh30X9Kc5ct1HyysrAw4eBJYurf2Yka1HCu2dmTtxpehKvc7//ZHvK3eFBLS7OY5uM9rgPG/c0H/AwmmORI7nRM4Joa1UKNE2sK1M2VBdKqc6SgCuNsf1A4Ox78y5Ot8jt22bPIV2dPcUKJXW8bsJpzoSWTm1Ro2t58SeG05zrNuyZcCrrwIpN5csGDhQ27lkCIVCgW5h3SyyZo7u9JM+fQBvb7NflohsGBe2l1d6OjB2rHYXzrIy7cip4cO1U1V1DWg6AO7O7iiuKAagXbdz5emVeKjzQ3VeQyNp9KY5PtDxAbi7uBuc76ZN2jxvcXa2/LINRCS/Y9ni1sEtAloY9ZlC5hflGwXX3xNRdqYfUOIPAFgatxM9WjWVN7FaXL5WiCvJ4vz5u0dYT3cTR3wRWbmDlw7iesl1IcYRX3Vzda3q9AK0T+OtdWSwJOlPP+E0RyKqiyRJ2H9xvxBjx5dlBQcDhw9XdSZJkrZdE3cXdwxuLvYyfbL7k9tOWdmavlVvgxVTTXPs3Rvw8THqVERkw45dFju+OgZxmqO1UigU8EZEZacXAOzcW1bHO+Q1b9kJoMKtKqAsx9MTrGenLnZ8EdXD8ePAb78BZ84AGo1lr70hdYPQbhPYxqhFbR1JV3FKPK5fB1JTazxUdidOAOfPizFOPyFyPEVFwDffAAcOAMXFdR+bcjUFeaV5Qow7OlqWu7v+zrsH6lh3WHd019Hso1h9ZnWd19Ad7dW+cXuj/p4lqeYNVIjI8WxY5Qek9QeKtJ0p7Piybq063BDap49b75SQxJXiLy++LU8gKthXpmz0seOLqB6WLgXuuw9o1Uo7Be3ZZy137Y1p4hy9Qc0GWe7iNioiAmjcWIzV9YVETrpP4aOigLZcaoHI4Rw9CkydCnTrpp0uFxMDqNU1H6s7zTHYMxgRPhEWyJKq033IUledGd1mNNoEthFi7257t9ZRX2evnsWyE8uE2L/u+JdR67glJwMZ4nrD7PgickAajYSkb58Bvt8MfHAV+CgTqkv95U6L6tCrh0poX0ttBo3G+qaxaDQSTu5qLsR69r8uTzK1YMcXUT0cOVL156Ii7VQ6SyguL8b2jO1CTHe6BOlTKIAuXcTYwYPy5HI7K1eKbe6yReSYjh6t+rNGA1RUAE5ONR+ru7B99/DuXNheBobUGaVCiVm9Zwmx3ed3663hCWinsj6x+gmUqksrY85KZzwY86BReerWmYgIoEOHmo8lIvt18HQWpKKAqkBBBOLacpd4azaqv/hQSyoKwK7jF2TKpnYrtqWg4mqkEPvXfaEyZVMzdnwR1UP1ji9A+yTeEnZm7hR+8VUqlOjftL9lLm7jDHkSL5fcXGDnTjE2cmTNxxKRfTOkzuy/JK7v1S2U0xzloFtnkpOBwsLaj5/QYQKa+DYRYu9te0/vuF+O/YL1qeuF2JPdnkSQZ5BRef75p9geMYIPWIgc0eptOmtrqPIR35Gjha1Z75gIKNyvCbEVW87XcrR8vvhF7Ixz8ruAsQNayZRNzdjxRXQb167pTxHo1Mky19ad5tg1tCv83Pwsc3EbV1PHl7UtcH/hgvhvycNDuwMlETke3Y6v2upMhaYCBy+JQ4u6h3Nhezl07gwoq/0mrdHo/z1W5+LkgpfiXxJi61PXY/3Zqk6uq8VX8dy654Rjwr3D8c7Ad4zKUa0G/P3FkeqjRhl1KiKycbv2iz3z3pFpcHZid4A1UyoV8G8uLlS8Y0+JTNnULjk3GfDIqWy3650CpdK6nrDwXzrRbRwTNz+BiwvQpk3Nx5qabscXpznWn27H17VrwLlz8uRSm06dtFNjMjKABQuAWbO0CyYTkWPRaMSpjkDtHV/JOckoKi8SYlzYXh4eHvprMt5uWv3DnR9GsGewEBv+y3B8eeBLJOUkYeLyicgpyhFen5cwDz4q47ZgdHICVq3SjjD+/XftOnIDBhh1KiKyccnHxbVaolpflycRMkjLDvlC+/RxT5kyqVn2jWycj5kOvBAC/CseiJ+Dhx50u/0bLcxZ7gSIrJ3u09t27Syzxtf1kut629VzYfv6i4oCAgKAq1erYgcOAE2bypZSrSIjgSeekDsLIpLLuXNAQYEYq63jS3dh+yjfKKOnwFHDdemi3Z33lttNq3d3ccfzcc/jpQ1VI7/KNeV4dNWjNR4/us1ojG4zusF5ensDY8Zof4jIMWWlip3unWKsa0QO1Syuuwp7lla1r5xtCo1GspoRVavPrIYECVBKQNROeLU4iun3Pnf7N1oYR3wR3YZc63ttSd8CjaSpbLs5uyE+Kt4yF7cDCoVtrPNFRKRbZwICgLCwmo/VW9g+jNMc5WRMnfm/nv+HhJYJtz3O29Ubc4fNNTIzIqIqV/OLUZbdVIgN6BlQ88FkVUb2DxfaUmFj7Eu+JFM2+laeFndQGRo9FCpnVS1Hy4cdX0S3Ud/pJ6a2MVWc5hgfGQ83Z+sbNmrN2PFFRLagpvW9alt8fPeF3UK7R3gPM2VF9aFbZ5KStLs/18XFyQUrxq/AC3Ev1HqMh4sHfrn3F0T4cOFpImq41TvSAan6VsEajOjdVKZsyBD974gC3PKE2B+bM2XKRlRSUYK/z/4txEa1ts6FJNnxRVQHtRo4flyMWarja0PaBqHNaY6G0/1CsmePdi0dIiJrUt+F7QvLCnEsW1x4smdETzNlRfWhu8C9Wg3s31/r4ZWclc7475D/4qd7fhIeagV6BOLVPq/izNNnMKLVCNMnTEQOadPuXKHt0jgDIQFeMmVDhtAucH9WiG3aVixTNqJNaZuEdUeVCmW9RjTLgWt8EdXhzBmgWOdzxRIdX5l5mTiZe1KIDWrOji9D9eoltvPytGuxdOwoTz63VFQAzvz0JaKb6juy+MClA1BL6sq2k8IJXUO71nwwWYSXl3YJhMOHq2I7dwJ9+9bv/RNjJqJXZC/8mvQronyjcHfru+Hu0vBdTiRJ+6DHyen2xxKR/Tt0WHzyG9T8EoCmsuRChmvfJR/bk6rayQetY5rqiqTVQrtXZC8EegTKlE3dOOKLqA66X0ZCQoDGjc1/3fWp64W2v5s/v9wYISwMaNJEjO3YIU8u1c2Zo90J7KWXgO3btSMEiMgxFRQAZ8UHubWuJbnn/B6h3SGoAzxdrWt3J0cUr7P8pqF1ppl/M7wU/xLGdxhvkk4vQNsRFxICTJmi3c1Rd/MEInIs6ad8hXbr9qUyZULGuLOfWOsL0lshv1Dev0ONRsLXU18EfloD7H0CyIvAqFbWOc0RYMcXUZ3qO/3E1HTnSg9uPhhOSj62NUb1LyStW1vHSKvERODkSeC//wX69AFmzpQ7IyKSyzFx5iKcnLS7B9dkzwWx44vTHK1D9ToTFKT9kVtiIpCbC/zwAzB2LBAbK3dGRCQXjUZC/rmmQqxnVw95kiGjTBnRCsDNUXvORUD4bmw+cbzO95jb92uSoL7SFEgZBqxZAHySic4q69062Aq+AhJZLzk6vtQatd6IryHRQ8x/YTv11FPA+PFAXBwQaAUjb1NTgYMHxdhdd8mTCxHJT7fOtGkDuNWyj8nu8+LC9rHh7M2wBgMHAt99p+0Ai46ufWMCS/rtN7HNOkPkuPYmXYJUIm4VPKx3LVsHk1VqEuKL8Emv4YLbOiDkMOBUgTMV/wUg34yghT/kCG2XoDQM6tJcpmxujx1fRHWQY0fHQ1mHcLX4qhC7s/md5r+wnYqLkzsD0e+/i+3AQKBfP3lyISL51bfOnM8/jwsFF4QYR3xZh+Bg7ZRCa5GUBCQni7H77pMnFyKS35od5wFU6+hS5aFXh3DZ8iHjDL8vB4sOVu2esjNzp2y5aDQSDm0SO7l63HkOSmUzmTK6PU51JKpFaan2l1mVqipW27orpqQ7zbF1o9Zo4teklqPJ1uh2fN1zj3VMvyQiebi4AAHV1qit7/pevipftA5sbcbMyFbp1pmwME51JHJkZ7IyAb+0yrZPVDqUSisYmkoGiY8SF5TckbkDkiTJksvSDadQcSVKiE2fHCJLLvXFr1tEtVCpgH37tDvwnT6tnY7S2gLfMXQ7vjjN0X5kZAB7xO+uuPdeeXIhIuvw+efAZ58BFy7UXWd01/fqHt4dSgWfX5I+3WmO994LKPlPhchhadovA/5vLFDiA2THYFDrsQAstHAxmUx8pNjxdbnwMlKvpSI6INriucz7PgtAm8q2c6NzGDfYuh/GseOL6DacnbULDde22LApFZQW6A1bZceX/Vi+XGz7+2vXhiEix6ZQABER2p/a6C1sH85pjqTv9Gn96bNjx8qTCxFZh8NZh7V/cMsHmmzHsMGTZc2HjNPcvzmCPINwufByZWxH5g6Ld3xpNBL2rRdnI3UZlAal0rpnKPH5D5EV2XpuK8o15ZVtF6UL+jftL19CZFK6T+Hvvls7zYmIqC4Vmgrsv7hfiMVGcO4a6dOd5hgUJO46SUSOpaC0AKevnBZid4TeIVM21BAKhUJv1NeOjB0WzyPxnzMozxHX8np8UmOL52EodnwRWRHdaY7xUfHwcvWSKRv7kpWlHXH1/PPaBe93Wng9yIsX9a/Jp/BEVB/HLx9HUXmREOOOjtansBDYvBl4910gIUH7v5am2/E1Zgzg5GT5PIjIOhzJFrcOdlI4oUNQB5myoYbqGRYPXOoE7H0C+P0n/DLrQYvnMPe7i0Lbye8CpiRYYGpUA3GqI5EVWXd2ndAe0pzTHE1l2DDg8OGq9rZtQK9elrt+YiJQff1JHx9g8GDLXZ+IbNfu87uFdnP/5mjsaf1PVx3N7NliZ1dBAfDqq5a7floacOCAGOMDFiLHdvDSQaHdPqg93JzdZMqGGsrvyhDgf89Xtm8AOJeVhyYhvhbLYfff4o6gnQacgVJp/buEcsQXkZVIv56uNxSZ63uZju5Ujx0WHhm8bJnYHjlS3DGUiKg2uut7cbSXddKtM/v2aXeItpRffxXbjRoB/fpZ7vpEZH0OZR0S2neEcJqjLRt/ZyvAuUSIfb/qdC1Hm96f21NQeqmlEHtkYkAtR1sXdnwR1WD5cuChh7S7bW3frn1qa25/nflLaAd6BHIOvgnpju7avh1Qqy1z7XPngH/+EWN8Ck/k2C5e1E5De+cdYPVq4NKl2o/VXcOjZwQXtrdGPXX+WkpLtZ1flvLTT2J79GjtBj1E5Lj+/HAE8PcHwNEJQE5rdA7qKndK1AA+nip4NxU7uv7aWGix689ZcF5oK32yMG20bUydZTkkqsHffwPff6/9AYDx44HFi817zTUpa4T20Oih3KrehPr2FdvXrgH79wOxFhg48fPPYtvPTzv1kogc14ED2inQiYnato+P9nNJqfOxn1OYgzNXzwixXpEWnKdN9ebvD8TEiLsqrlsH9O5t/msfOQIcOybGJk40/3WJyHrl3SjF1Z2jAE3VTkpOfY/U8Q6yBR26X8WulKr2kR3BFrluhVqDvX+Jo73uuPMknJ1CLHL9huK3aqIaHBSnw6NLF/Ner6SiBJvSNgmxhJYJ5r2og4mIANq3F2Nr15r/upIE/PijGLv/fk5zJHJ0unXmjjv0O70AYGemuCuGh4sHOgV3MmNm1BB33SW2LVFnAP06ExnJaY5Eju7PbalCpxcAjBnQXKZsyFQmjPYX2sWZbXH07GWzX/fzpUegvi6u5fXSE6Fmv66psOOLSEd5ufi0FtB+ITGnbee2CTt2KaDg+l5mIMcXksJCoEMHsaNr8mTzX5eIrNshcdmVWuvMjkxxmmNseCxcnFxqPphkp1tn9u8HLpv/+wj8/YGgoKr2xIk1d6QSkeNYvyNHaLs0TkN4Y2+ZsiFTmTqqHaDKF2LzFpt/na91pzcBwVUjBt3CTuP+Qa3Nfl1TYUkk0nHypP5itObu+FpzRpzmGBsRi0CPQPNe1AHpfiHZswe4csW81/Ty0i44nJUFLFoEjBtn2d0kicg61XdksW7HV3xkfM0HklWIjwc8PcXY+vXmv+6rrwIXLmjXixs/Hpg0yfzXJCLrdkCnzoS0yJInETIpDzcXhMYkCbG/1ynMes2i8iLsUr0FPNEZeLwTEPchhk9KNes1TY0dX0Q6dJ/CR0Vpd0Yyp79SxIXth7XgAlDm0Ls34OFR1ZYky3whAbTrek2bBixZAijMW5uIyMrl5gKZmWKspgcspRWl2H9xvxCLj2LHlzVzdQUGDRJjlpru6OwMJCRo1yRt184y1yQi63XupLjbXvtOFtxmlsxq4J1lQjvjYBuUlZtv164VJ1egoOzmbm8hR6G862V8/mqM2a5nDuz4ItJh6fW9Uq+l4tSVU0KM63uZh5sbMGCAGLPUFxIiolt0H7C4uQFt2ugfd+DSAZSpq365VUDBHR1tgO7o4nXrAI1GnlyIyDGVlFWgMDNaiPWN5TRHe/HEOHGtNqmoEX5ed9Js1/vxqLiQ5KBmgxDmHWa265kDO76IdNR33RVT+euMONoryDMIXULN3NvmwGpa54tfSIjIknTrTEyMdrSOrh0Z4jTH9kHt4efmZ77EyCSGDhXbOTn6f+dEROa0bnc6UOEuxEb3byJPMmRy8TERcA05K8R+XJ5Ty9ENk30jG3+f/VuITYqxvfn07PgiqkajAQ4fFmPmHvG1JkVc3+uuFndBqeD/Nc1Ft+MrO1t/MwMiInPS7QTh+l72pXlzoFUrMcbRxURkSWu3ZwttJ7+LaNuU6wfbkw5x4poJ+/4xz9o83x7+Fmqpahqlh4sH7ml7j1muZU78dk1UTWoqkC9ukmHWEV/F5cXYnLZZiHF9L/Nq0QKIFkd+m+ULyZ49wLVrpj8vEdk+3Sn1NdUZSZKwM3OnEGPHl+2wxC7CaWnAqVO3P46IHM/e/eVCu3F0Zi1Hkq0aO0qcunojtR3OXjDtl4+Ssgp8/PNRQFO1QPHYdmPh5epl0utYAju+iKrRfQrfuDEQZsbpy1vSt6C4oriyrVQoMSR6iPkuSADM/4WkogK4914gPBx49FGOKCOiKgUFwJkzYqymjq+UqynIKRKnLfSK5JawtkK3zuzaBVy/btprvPuudm24IUOAP/8E1OZb15iIbExqsq/Qbt2huJYjyVY9MbY94FJUFZCcMG9Jskmv8eb/9iPni1+A+cnA7qeBEm9M7z7dpNewFHZ8EVVz4IDY7tLFvDvw/XnqT6EdFxGHAPeAWo4mU9H9QpKWBpSV1XysMVas0G4rX1wMfPkl0KkTcNJ8600SkQ05fFi7o+wtTk5Ax476x+lOcwz2DEZz/+b6B5JV6tcPUKmq2i4uwIkTpjv/1avAzz9r/7x+PXD33cAnn5ju/ERkuyrUGlxPF+tFfKx7LUeTrfLzckPjdmJh2bjvokmvsegLF+0frrQG1n4O7+Wb0CO8h0mvYSns+CKqZt8+sW3O9b0kScLK0yuF2MhWI813QarUvz/g6wvcfz+wZg1w9qx2C3pTmTtXbMfF1bxjGxE5Ht060769dldHXXrTHKPioTDnkxgyKQ8P7S7CvXoBixYBWVlAvAlnqn79NVBSUtV2cQEefNB05yci27V+XzpQIo74Gt0/Sp5kyKz63VkE+KYD/d4CnolGSsfJyC/Nv+376uPP7Sm4ltRViD0wuaSWo61fDXsIETkmjQbYv1+M9TBjh/ahrEO4UHBBiI1qPcp8F6RKXl7ApUuAuxkefh07BmzdKsaeesr01yEi26Tb8VVbndmesV1oc30v27N8uXnqjFoNLFggxsaOBUJCTH8tIrI9f266BKBqxJfSJwtdW/MDwh59PKstEr0ioIZ2TbfiCmDZiWV4pMsjDT73q3MuAGhR2VZ45mDOs90afF65cMQX0U1nzugvbN+9u/mut/KUONor2j8abQI5LMhSzPFlBADmzxfbwcHaLyRERIB+x1dNdeZy4WUk54rrdPSO6m3GrMgczFVn1qwB0tPF2NNPm+daRGR7du2pENpBLc9BqeSIYXsUGRCEEa0ThNh3h79r8HkzsvNwfL042ivu7hPw86phiLqNYMcX0U3e3sA77wCjRgGhodqf8HDzXe/P0+L6XqNaj+I0FhuXnQ38+KMYe/RR006jJCLbJUnAc88BkycDbdtq15CsqePrn3P/CG0vVy90CTXj3HuyGZIEfPihGOvSBejZU558iMj6lEUnArGfARG7AKcStO/Mhe3t2UOdHxLaOzJ34MyVMzUfXE9PvH0IKKu2c6NCjY9fadWgc8qNUx2JbgoLA157rap9zbS7wQrO55/HwUvifvZc38v2vfsuUFRtcxVnZ+Cxx+TLh4isi0IBTJ+u/QG0o4w9PfWP25ouzpeOj4yHs5K/shHw99/AP2K/KJ56yrwb8RCR7ShXlyOt0f+AYTfXYlI746l7/5I3KTKrhJYJCPQIRG5RbmXs+yPf4z8D/2PU+TKy8/DXdzFCLKz7PsS2t+0nLBzxRVQLf3/znXvV6VVC28/Nj9NYrISx28GnpwNffCHGJk0y76hBIrJtPj7aXR11bT0ndnz1a9LPQhmRJRhbZzQa4JVXxFhkJDBhQsNzIiL7cCLnBEoqqi1A7lSBPi3vkC8hMjtXJ1c82FHc3eS7Qz+gvMK4YvPgi4cgFQUIsfde9zE6P2vBji8iGeju5jisxTC4OLnIlA0VFwOLFwNDhwLjxxt3jrfeAsrLq9qursCbb5okPSJyIFeKruDY5WNCrG+TvjJlQ6YiScC2bcDUqUDTptq6Y6jly4GD4mBxvPlmzbuCEpFj2ndBXEiyuX9zNPJoJFM2ZCmV0x2vNgM2v4kL7/yDj34+bPB5TqTlYNtScQH7sB67MWV4u4YnKTOOmyeysBtlN7AxdaMQ426O8tm2DRgxompjA2dnIDNT+xS9vpKSgB9+EGNPPAFEcedoIjLQtoxtQtvd2R3dw8240wqZXXEx0LkzcPp0VWzxYuBf/6r/OSoqxOUYAKBNG+16cUREt+y9sFdodw9j/XAEnUI6odGmZbjyz32Vsc/nZ2HmFMPO8+BzSUBZtVHmCjUWfhRooizlxRFfRBa2/ux6lKpLK9vOSmfc1eIuGTNybDExQFlZVbuiApg507BzvP66dgrKLZ6e+tNRiIjqQ3d9r7jIOLg6cYcMW+buDjRpIsb+/W/gxo36n+PHH4FTp8TYO+9oH9YQEd2y76I44osdX46jb5cgoX1pX0/M+/VIvd+/6/gFHF4lruMV3X8XRvVuYZL85MaOLyJoF7KXJMtca/nJ5UK7b5O+8HPzs8zFSY+vr/5T919+AXbtqt/7//gD+P13Mfbcc0BQUI2HE5GDKigQp0PX5p8MceVyru9lH557TmxfvAi8/3793nvxIvDyy2KsSxdgzBjT5EZE9iGvqAjHMtOFGEcMO46PXuwEhbu4O9tLL7iiQq2p5R1VKtQajJp4EVCrqoJOZfju06YmzlI+7PgiAjBggLajIiEBeOMN4OxZ81ynTF2GlafE9b3uaXOPeS5G9fbmm9oOsOqefVYcxVWT3Fz9TjN/f+CFF0yaHhHZgTlztIvZ9+ql/XzZuFH/mLySPBzOOizE2PFlH+66S7uOZHUffgicO3f79z7yCJCTI8beew9Q8rd4Iqpmybqz0Lx3FZh3Akj8For9T6BLaBe50yILaRbmhzFPHBVixRlt8eg7O2773rEvbkTuUbGTNGb4LvSOiTBpjnJiySSHV1QEHD+u7cT46y/g7be1T1fNYVPaJuSV5gkxdnzJr3FjbYdndfv2AT/9VPf7AgOBTz4BPDyqYu+/r9+JRkS0bx9QUqIdTfr558D69frHbM/YDo1U1eOuclIhNiLWglmSuSgUwMcfi7t4lpQAL710+/e+9x7QqlVV++67gSFDTJ8jEdm2df9cBaAEctsBRx6C64Hn4OXqJXdaZEE/vNsLriHiCI7vP26Fi7kFtb5nV+YurHJ7AGhbNYVF6Z2NJZ+1N1uecmDHFzm8w4fFrcWVSu0UAnP4PUmcE9czoifCfcLNczEyyPTp4hcLQDu15Haj/6ZMAQ4cADp2BO6/X/tknoioOkkC9u8XY91rmH2y9Zy4vldsRCzcnLlln71o10678Ul1y5YBv/1W9/s6d9bWmQcf1G688s032o40IqLqDh0UF/2LbJslUyYkFw83F/z7XXG6o6YgGP3HH0ZZuVrv+CtFVzDh9wlQq3KB+8cCCU8CzkV4f8FFtG1qH4va38KOL3J4+8Q1ING+vXZxclNTa9T449QfQuzetvea/kJkFFdX4KOPxFhWFtCzJ7DjNiOE27QB9uwBvvqKX0aISN/Zs9q1JKvr0UP/ON2OL05ztD9vvqmdEl/d/fdr609da416eWl3D96/HwgIMGuKRGSjLpwMFdrdulloAWOyKq/9qxsadxa/4J7Z2AdN4vbj8rXCytjW9K3osqgLzuXdnHOvANBjIWYsnYsXHrzDghlbBju+yOHtFXf9rfEpvClsy9iG3KJcITamLVemtSbDh+uvwZKbCwwaBDz2GDB/vv6X11vc3QFvb/PnSES2R7fOBAcDETrLZuSV5OHAxQNCjB1f9qdRI+Ctt8SYJGnXhhw9WrtLcEpKze9VKLhxChHVLO3idZRfbi7EhvW3rxE7VH/fL2wEOJUJsawDsWjS8TzuGL0Vw96dg/7fDkRGXoZwTN8mffH+6OctmarFsOOLHN7OnWK7pqfwpqA7zbFzSGc0929ey9EkB4UC+O477bTF6kpLgUWLgAULADfOOiIiA9VUZ3RHh249txVqqWoagspJhV6RvSyQHVna9OnA1Kn68T//1K4TeeOG5XMiItv245ozYsC5BKP7RsuTDMluWM/meHr2fr3Or5ILrXF4RT+s3ZoLKMVdvEK9QvHzmJ/hrBSnzNoLozq+5s+fj6ZNm8LNzQ2xsbHYq/sos5ovv/wSffr0gb+/P/z9/TF48OA6jyeypIsXgfR0MdbLDN8zNJIGiScThRinOVqnkBBg+3btDly6kpLqtxAxNRzrDNkT3Y6vmurM+rPiavfxUfFwd3E3Y1YkF6US+PJL7aL1usrKgAceAIqLLZ+XI2KtIXuxfmuh0PZueho+niqZsiFr8PmLvfDpT0lQuNcwXWXje8ClTpXN/k37Y88jexDhYz+7OOoyuONr6dKlmDFjBt544w0cPHgQnTp1wtChQ3H58uUaj9+yZQsmTJiAzZs3Y9euXYiMjMSQIUNw4cKFBidP1FC7doltHx/t4rOmtvfCXlwoEP/Nc5qj9fLxAVauBJ58Uv+1efO0u3+S+bDOkD25cQM4ckSM1dTxtSFtg9Ae3GywGbMiuSkUwKxZwNKlgErnu2lyMh+yWAJrDdmTEwd9hHbbLldkyoSsybPjO+Ovzdfh0jhdfEHjCvz+C5zV3vhg8AfYOHkjIn0jZcnRUhSSVNdSmvpiY2PRvXt3zJs3DwCg0WgQGRmJp59+GjNnzrzt+9VqNfz9/TFv3jxMnjy5XtfMz8+Hr68v8vLy4OPjc/s3ENXT889rtxe/ZcgQYN0601/nhb9fwEe7qlZObxPYBsnTk01/ITIpSQJWrwY2bQKKirRbzzs5AWPHAsOGyZ1dw1jz5yrrDNmTTZu06wTe4uwM5OUBHh5VsfP55xH5ifgL595H9qJ7uJkWnSSrkpQE/PwzcP26ts6UlgLdugFPP62tObbK2j9XLV1rrP2/B9mukrIKuHuXAGVelbFZ8/bgvemxMmZF1iQjOw9PvH0ImRkKlJUqUFamRHBoBd6dGYqBnVrLnZ7RDPlcNWgCZ1lZGQ4cOIBZs2ZVxpRKJQYPHoxdukNnalFUVITy8nIE1LElTWlpKUpLSyvb+fn5hqRJVG+600/i401/DY2kwdITS4XYmDYc7WULFApgxAjtD1kG6wzZG90606WL2OkFABtTNwptPzc/dAntYubMyFq0awe8+67cWTgWS9Qa1hmylMQtKUBZGyE2MYHrCFOVqGBfrJ7fX+40ZGXQVMfc3Fyo1WoEBwcL8eDgYGRlZdXrHC+//DLCwsIweHDtQ/hnz54NX1/fyp/ISPsedkfyKCkBDogbaJllfa8dGTtwPv+8EJvQcYLpL0RkB1hnyN7UZ30v3WmOA5sNhJPShof6EFk5S9Qa1hmylMT12ULbpXE62jdrLFM2RNbJors6zpkzB0uWLEFiYiLc6tgabdasWcjLy6v8yczMtGCW5Cj27wfKy6vaSqV5dnRcfHyx0G7fuD06BHUw/YWIiHWGrIpGo7+WpG7HlyRJ2JDK9b2IbEl9ag3rDFnK3t3iJK7I9udrOZLIcRk01TEwMBBOTk7IzhZ7lbOzsxESElLnez/88EPMmTMHGzZsQExMTJ3HqlQqqHRX+iQyMd2n8B07ahc1N6VydTl+TfpViE3owNFeRLVhnSF7kpysXbepurg4sZ2Uk4SsG+IIkzuj7zRvYkQOzhK1hnWGLOX8iSZCOy5OI1MmRNbLoBFfrq6u6Nq1KzZurFqLQqPRYOPGjYjT/U2umg8++ADvvPMO1q5di27duhmfLZEJ1Wf6SUNtStuE3KJcITauwzjTX4jITrDOkD3RrTNRUUCEzk7h61PXC+0mvk0Q7R9t5syIHBtrDdmL/ScvQX1NLCz3Dg2VKRsi62XQiC8AmDFjBqZMmYJu3bqhR48e+PTTT1FYWIiHH34YADB58mSEh4dj9uzZAID3338fr7/+On755Rc0bdq0ct68l5cXvLy8ar0OkbkdPSq2zdHxpTvNsXtYd7QIaGH6CxHZEdYZshf1qTN60xybD4ZCoTBjVkQEsNaQfUjcehZAtY4uVR5G9ubDEyJdBnd8jRs3Djk5OXj99deRlZWFzp07Y+3atZWLQ2ZkZECprBpItnDhQpSVlWHs2LHCed544w28+eabDcueqAFOnwaOHdM+kd+5E+jd27TnL6koQeLJRCHGaY5Et8c6Q/bi88+B55+vqjN9+4qvl6nLsPXcViE2uDnX9yKyBNYasgfFTZcDL48EzvcEMnuhmU8rODtxdgmRLoUkSZLcSdxOfn4+fH19kZeXBx9TL8JEZCaJyYkYs2xMZVsBBTKfy0S4T7iMWRFp8XNVxP8eJIfNaZsx8IeBQiz7hWwEeQbJlBGR6fBzVcT/HmQOPb/qiT0X9lS23+r/Fl7v97qMGRFZjiGfqxbd1ZHIkfx87Geh3bdJX3Z6ERFRpdVnVgvtrqFd2elFRET1UlxejIOXDgqxXpFmWLuFyA6w44vIDK4UXcHK0yuF2PgO42XKhoiIrJFux9fwlsNlyoSIiGzN3gt7Ua4pr2wrFUrEhsfKmBGR9WLHF5EZLDm+BGXqssq2q5MrxrXnfHsiItJKvZaKk7knhVhCywSZsiEiIluzOX2z0O4c0hneKm+ZsiGybuz4IjKD7498L7RHtxkNf3d/mbIhIiJrs+bMGqHd2KMxuod3lykbIiKyNbodXwOaDpApEyLrZ/CujkS2bsECoKgIGDAA6NwZcHIy7flPXD6BfRf3CbGHOj1k2osQEZHV2r4dWLUKGDgQiI8HPD31j9Gd5jis5TAoFXweSUREt3c1vxjb508CmoQBTbcA3lno37S/3GkRWS3+hkUO57PPgBdfBLp1AwIDgcRE055fd7RXqFco7oy+07QXISIiq7V8OfD++8DQoYC/P/DMM+LrReVF2JwmPqlPaMFpjkREVD9fr0iGZv8jwO+LgY8uAQuOomdoH7nTIrJa7Pgih3LxInD6dFX7+nUgKsp056/QVODHoz8KsUkxk+Cs5OBKIiJHsblan1Z5OdCokfj6prRNKFWXVradFE4Y2mKohbIjIiJbt2JdvtD29FAi0NtXpmyIrB87vsihbBYfsMPPTzvd0VTWn12PrBtZQmxK5ymmuwAREVm1q1eBI0fE2ACdZVdWnxanOcZHxcPPzc+8iRERkd04uidAaLfvkSNTJkS2gR1f5FB0O7769jXtGl/fHv5WaHcP6452jduZ7gJERGTVtm4FJKmq7eYGxFbbXV6SJKxJERe25zRHIiKqr6yrN1CQ2laIjRzqJVM2RLaBHV/kUHQ7vnSfwjdE9o1s/HHyDyH2UOeHTHcBIiKyerp1pndvQKWqah+/fBwZeRnCMcNbDbdAZkREZA++XnES0LhUBZTleGR0G/kSIrIB7Pgih5GRAaSmijFTdnx9fehrlGvKK9vuzu6Y0GGC6S5ARERW73YPWH5P/l1oR/lGoX3j9mbOioiI7MXKdTeEtlezkwgJ4Igvorqw44schu6XkUaNgI4dTXNutUaNRQcWCbHxHcbD393fNBcgIiKrl5MDHD8uxnQ7vpYnLxfa97S5BwqFwsyZERGRvTi+N1Box8RekSkTItvBji9yGLodX/36AUoT/T9gbcpanMs7J8Qe7/a4aU5OREQ2YcsWse3pCXTrVtU+c+UMjl0+Jhxzb9t7zZ8YERHZhfM5+Sg8J05rvPsuH5myIbId7PgihyBJwKZNYsyU0xy/OPCF0L4j5A50D+tuugsQEZHV060zffoALtWWYdGd5hjsGYxekb0skBkREdmDRctPAhrnqoBTGf41iut7Ed0OO77IISQlAZmZYsxUHV/nrp/T25r+iW5PcOoKEZEDkSTgr7/E2O3W97qnzT1wUppwa2EiIrJriSuLhbZPdDICfT1kyobIdrDjixzCarFfClFRQLt2pjn3ogOLIKFq73pvV29M6MhF7YmIHElyMnBOnPGOhISqP5+7fg77L+4XXr+3Hac5EhFR/Wg0Ek7uai7E4gZckykbItvCji9yCGvWiO2EBMAUA7JKKkrw1aGvhNjkTpPh5cqdVYiIHInuA5bISKB9tc0adRe1D3APQL8m/SyQGRER2YNVO8+i4mqkEHtkXJhM2RDZFnZ8kd27fh3Yvl2MDR9umnP/fPRnXC68LMQe6/qYaU5OREQ243YPWJafFDu+RrUeBRcnFxAREdXHoiXnhbaT30WM6ddSpmyIbAs7vsju/f03oFZXtVUq06zvJUkSPt79sRAb2GwgOgZ3bPjJiYjIZuTl1f2AJetGFnZk7BBe526ORERkiB0bfYV2y9gUKJVcU5ioPtjxRXavXz/gf/8DRo0CPDy0nV6eng0/79qUtUjKSRJiz8c93/ATExGRTXFzA379FZg2DQgLA1xdgYEDq15fdmKZ3lqQdza/U4ZMiYjIFuWV5KGgz9NAn3eBkEMAgDF3q2TOish2ON/+ECLbFhwMPPqo9qe0FMjJMc15dUd7tQ1si7ta3GWakxMRkc1QqYDRo7U/kgSkpYkPWH48+qNw/MjWI6Fy5hcWIiKqn/Wp66EO3wGE7wAGvQaXwqb4vweOyZ0Wkc3giC9yKCoVEBHR8PMcyTqCDakbhNiMuBlQKvh/KSIiR6ZQAM2rbbqVnJOst5vj5JjJFs6KiIhs2Zoz4kKS/Tu2RGNfbqZFVF/8lk5kBN3RXkGeQXgw5kGZsiEiImulO9orxCsEg5oPkikbIiKyNRpJg79S/hJiw1uaaKcuIgfBji8iA527fg6Ljy0WYtO7T4ebs5tMGRERkTXSSBr8dPQnITax40Q4K7nSBBER1c+hS4eQdSNLiCW0TJApGyLbxI4vIgPN3j4b5Zryyrabsxue6PaEjBkREZE12pq+FZn5mUJsUswkmbIhIiJbtOLUCqHdMqAlWjZqKVM2RLaJHV9kt9auBVau1C5obyoZeRn45tA3QuzRLo+isWdj012EiIhsQloasGgRkJtb8+s/HP1BaMcEx6BTSCcLZEZERPZAo5HwxVx3IKdNZWxEqxEyZkRkm9jxRXbrzTeBUaOAkBBg6lTg6NGGn3P2NnG0l8pJhZd7v9zwExMRkc356Sfgsce0dWbYMOD336teKyovwm9JvwnHc7QXEREZYumGU8hJnAXMTwYWHga2zcQ9LcbLnRaRzWHHF9mltDRgzx7tn69fB775Brh4sWHnzMzLxNeHvhZij3Z9FGHeYQ07MRER2aQlS7T/q1ZrRxlv2VL1WmJyIm6U3ahsKxVKPNDxAcsmSERENu3Tr6ut7ZXdCc6HnkR8s+7yJURko9jxRXZp2TKx3agRMKiBm2jpru2lclLh5XiO9iIickTHjwNJSWJs3LiqPy/cv1B4bXDzwXxQQkRE9abRSDiwvoUQ6zbkLJRKhUwZEdkudnyRXbr1FP6We+8FXFyMP1/69XR8dfArITatyzSE+4Qbf1IiIrJZunUmIgLo1Uv750OXDmFH5g7h9al3TLVQZkREZA++WXUC6msRQuy5R0JlyobItrHji+zOyZPA4cNibHwDp8K/svEVYbSXq5MrZvae2bCTEhGRTZIk/Y6vceMA5c3fqubvmy+8FuYdhnva3GOh7IiIyB7M/+aK0HYNSsXYAa1kyobItrHji+zO0qViOyQE6NvX+PPtvbAXi48vFmKPd32co72IiBzUgQPA2bNi7NYDlqvFV/HLsV+E1x7r+hhcnBow7JiIiBxKWbkaRze3FmJxwzI4zZHISOz4IrtS01P4++8HnJyMPZ+E5/9+Xoj5qnzxer/XjcyQiIhsnW6diY4GunbV/vnbQ9+iuKK48jVnpTOmdZlmweyIiMjWLfj9GDT5IULshWmRMmVDZPvY8UV25cAB7VTH6qovNmyoxJOJ2J6xXYi91vc1NPJoZPxJiYjIZpWXA4vFQcAYNw5QKACNpMGC/QuE18a2G4tQb67JQkRE9bfgqwKh7RZxCiPio2XKhsj2seOL7Mp8cVkVREUBPXsad64ydRle3iDu2tjMrxme7vG0kdkREZGtW7ECuHhRjN2a5rg2ZS1Sr6UKr03vPt1CmRERkT04e+EazmztJsT6Db8kUzZE9oEdX2Q3rlzRn34ybVrVYsOG+nDnh0i5miLE5gyeA5WzysgMiYjI1uk+YOnZE+jYUfvn93e8L7zWKbgT4iPjLZQZERHZg/+bcwSocK8KOJXhgxfbyZcQkR1gxxfZjW++AUpKqtouLtqOL2OcuXIGb299W4j1jOiJ+9rd14AMiYjIlp04AWzZIsam3xzQtTV9K/4594/w2lM9noJCwYWIiYiofirUGqxd3FyIRfXcj5joIJkyIrIP7Pgiu6BWAwsXirH77gOCgw0/lyRJeHz14yhVl1bGlAol5g6byy8wREQObIG4fBcaN9bWGgB4a+tbwmsRPhGYFDPJQpkREZE9eO+bg6i4EiXEXn7OU6ZsiOwHO77ILqxdC6SlibHpRi6r8sORH7ApbZMQe6bHM+gW1q2WdxARkb3Lzwd++EGMTZsGqFTAtnPbsDl9s/DazPiZnBpPREQGmbdAEtpuESfx+D0xMmVDZD/Y8UV2Yd06sd25MxAXZ/h5cgpz8PzfzwuxKN8ovDPwHeOTIyIim7d9O1BUVNVWKoHHHtP++e1/xKnxYd5hmNplqgWzIyIiW3fkXBpyToo7N46dchlKJWecEDUUO77ILnz2GbBzJzBxonZtr+nTtVvLG0KSJEz9cyquFF8R4vMT5sPL1cuE2RIRka1JSNCOLH7lFe0Ux1GjtDsH78zciQ2pG4RjZ8bPhJuzm0yZEhGRLfrh5DxgRiQwchoQfARwy8NHz3eVOy0iu8COL7ILCoV2hNdPPwHnz2s7wAy1cP9CrDy9UoiNbTcWI1qNMFGWRERky6KigHffBTIztet9SZKEWRtnCceEeoViWlcjd1YhIiKHdKngEhbuXwi4FgFdvwIe74yHv/gUQf5c34vIFJzlToDI1IKM2PTk+OXjelMcG3s0xtxhc02UFRER2QuVCggNBX4++oveTo4vxb/E0V5ERGSQd/55B8UVxZVtJ6UTXhlpxJN8IqoRR3yRwysuL8aE3yegpKJEiH83+juEeIXIlBUREVmz/NJ8vLD+BSHW1K8pHuv6mEwZERGRLTp79Sy+PPilEJt6x1S0CGghU0ZE9ocdX+TQJEnC46sfx/HLx4X4s7HPIqFlgkxZERGRtXtry1vIupElxD4d+incXdxlyoiIiGzR61teR4WmorLt5uyG1/u9LmNGRPaHHV9kkyQJWLoUKC1t2Hk+2PEBfjgi7k/fKbgT3h/8fsNOTERENm/9euDSJf348cvH8dmez4RYQssEjGo9ykKZERGRPfhj20n88leKEHuq+1MI9wmXKSMi+8SOL7JJiYnA+PFA69bA998DarXh5/jj5B96ixJ7unhi8b2LoXJWmShTIiKyRZcuAfffD0RHA7NmAdeuaePl6nI8uvJRqKWqwuPq5IrP7voMCkO3EyYiIoel0UiY+kQh8NUeYMnvwOW28FH5YGbvmXKnRmR32PFFNic7G3js5hIq584BDz2k3VbeEAcuHsDE5RMhQaqMKaDA4nsXo23jtqZLloiIbI4kAVOnAtevA8XFwJw52g6wK1eA1za9hl3ndwnHv9TrJa7FQkREBhn/8j+4eqKrtnFyDLDwGEZovkQjj0byJkZkh9jxRTZFkoBp04DcXDE+0YBNT45kHcGQn4agqLxIiH9w5wcY2XqkCbIkIiJbtmgR8NdfYmzYMGD3ldX4YOcHQry5f3PM6iOOHiYiIqrLX7tT8etn3YWY0vsyPnqcawwTmQM7vsimfP01sHKlGLvnHmDChPq9/2j2UQz6YRCuFl8V4g93fhjPxz1voiyJiMhWnTkDzJghxkJDgZnvXsSUP6YIcVcnVywbuwweLh4WzJCIiGxZUUk57ptQDJSLteM/n11ESICXTFkR2Td2fJHNOH0aeO45MRYcDPzvf0B9llU5knUEg34YhCvFV4T4wGYDsXD4Qq7NQkTk4EpLgcmTgSJxQDDmflGEqetH69WPD+/8EF3DulowQyIisnV3PboDhenthVjHEVsx6yHWEyJzYccX2YTMTGDIEODGDTH+1VdA48a3f//KUyvR+9veyC0S50j2b9off47/k4vZExE5uIoK7ejh3bvF+NRp5fjsyl3Yd3GfEB/Tdgye6vGUBTMkIiJb9+Cr/2Dbj/2FmEtQGjb91L3mNxCRSbDji6ze5cvAnXdqF7Kv7pFHgBEj6n6vJEn4cOeHuHvJ3bhRJvaa9W3SF6smrIKnq6eJMyYiIlui0WgXs09MFOPRLTRI7TEG2zK2CfGmfk3x9aivOVKYiIjq7en3d+Ln93qLQWUF/vd1EQJ9OWWeyJyc5U6AqC5XrwJDhwKnTonxO+4APvmk7vdeKbqCJ9c8iWUnlum91rdJX6x+YDU7vYiIHJxGAzzzDPDDD2Lc00uDgElPYvOFVUI8yDMIf038C35ufpZLkoiIbNor8/dg3ivdoTvuZPTTO/DwiH7yJEXkQNjxRVZrzx7g/vuBjAwx3qYNsG4d4FXH2o8rTq7AY6seQ3Zhtt5rk2ImYdHIRXBzdjNxxkREZEtyc4EHH9TWlOpUKg08p0zAPkl8cBLgHoANkzagTWAbC2ZJRES2qqxcjSHTtmHr932h2+nVZ9IWJH7aX5a8iBwNO77I6kgSMHcu8MILQHm5+FrTpsCGDbWv65Wck4xXN72KxJOJNb4+e9BsvBz/MqenEBE5uJ07tQ9XLlwQ40onNdT33YvLjVcIcV+VL9ZPWo+OwR0tmCUREdmq42mX0XfEeVxL6q/3WqdRW7HlO470IrIUdnyR1VEogD//1O/0CgvTdnqFh+u/J+VqCt7d9i5+OPIDNJJG73V/N398c/c3GN1mtHmSJiIim7J3r36nFxRqaEZPhKaF2OkV4hWCFeNXoEtoF8slSERENqmgtACf7v4U76/4E4XJu/VebzloG/Yv7wOlkg/iiSyFHV9kld59F9i4sardpw+wZIm28+uW0opS/HnqT/zvwP+wMW2j/kluGtFqBBaNWIRQ71AzZkxERLbk8ceBDz/S4ML5m1NPvC4B904Amm0VjusT1QdLxy5lDSEiojqdvXoWPx/7GXP3ztXuJO8LoNMPwOGHtQcoK5Dw+Has+LwvnJ24xxyRJbHjiyxOkoD0dGDbNqB5c6B3b/1jYmOBu+8GVqwAZs0C3n4bcHKSkHotDZvSNmH1mdXYkLpBb6fG6gLcA/DxkI8xudNkTm0kInIw164BO3ZoN0mZPLkqfiH/AjambcT61PXI7e4LnJ8HNNsI3PsA4HVZOMeMnjMwZ/AcuDi5WDh7IiKydvmFpVj892ms+DsXVzr/G3uzdugf1O8t4OhEKL2vYN7XOXji3v4Wz5OIjOz4mj9/Pv773/8iKysLnTp1wty5c9GjR49aj//111/x73//G+np6WjZsiXef/99JCQkGJ00WT9J0n7puHBB+3PuHHDyJJCcDBw7Bly8qD1uyhT9jq/i8mJk3cjC3U9eRpNBV1HRZjNGLDmK/Rf340rxldte29PFEzPiZuD5uOfh6+ZrhrsjInNjnaH6KCqqqjOZmcDp09o6cyJJwslk7QMPb/8SnAr/D47nHMPR7KNIv55edYIOTkCpBuj2P0BZNU2+W1g3fDzkY/Rp0sfCd0RElsRaQ7dTVFKOE2m5OHLmCpLPFiDpdCnOnHJGVrofCjOjgYqb6z4+UgZE6L9f4Z+BuKe+xlfP3Ye2TWMsmzwRVTK442vp0qWYMWMGvvjiC8TGxuLTTz/F0KFDcerUKQQFBekdv3PnTkyYMAGzZ8/GiBEj8Msvv2D06NE4ePAgOnToYJKbqM2W9C3IL81H8kF/5F93Neoc/oGlaBVzvcbXUo774spl43YG9PYtR7uuV2t8Lf2UN7IveNR9AqnmEUxuHhXoFJdbdZgkVf4586wXMlO8IUkKSJIEjUZ7DrUaN2OARq2ARqOARq2AWg2oK5RQqwEXlRpDxqdUnk+CBI2kgSRJ2PlXE2xJjEZJoTOKClxQmK9CYb4KGvXth/D+tjYLF3+cjOsl13G95DouF15GXmmeeNDO254GgHaE18OdH8ZL8S8hyFP/3yIR2QZbqjNJOUlIuZqCSxkeyEjxNuocSgXQfYD+DrQAkHPJDanJxnfgd+1zGc4ukl78+hVXnDriX/ebdepM9XoS0zMH7p5qbRxV8YLrzji6uzEgAZqbYY1a+2eNpvqPtsZo1EBFhQIVFUB5uQK9Rp6Cp38h1Bo11JIaFZoKVGgqkJXhhRUfD0ZJkQtKCl1RXOCOkgIPqMtqq+1VuRdcc8N7ib8BjU/pH+akBnosrGxG+kRi9qDZmNBxApQKTkMhsme2UmuuFl/F9oztKC50wtE9gUafp3Wna/BrVKYXLy9T4OB2439vjm6Xh8CQkhpf27spGPoVSFTD0rwAgMiWeQiNKtIeU+37hyRJ2L81GGUlSm09kbQP3NVqSftdRiNBrQYq1BLUagkVaqC8QkJ5mYTyCiCywzmEtE1DaUUpStWlKKkoQXFFMYrLi7Htk8dwI9cf5cUqlBd6oaLQFyj1ARB686cOGX2AiH1C6O7Wd+OdAe9wUxQiK6CQqv8mWw+xsbHo3r075s2bBwDQaDSIjIzE008/jZkzZ+odP27cOBQWFmLVqlWVsZ49e6Jz58744osv6nXN/Px8+Pr6Ii8vDz4+PvXONWZhDI5dPgZ8vwFIG1Tv9wnaLAfG31vza7/9AhyfYNx5o7YB/+pb82trPgP2PmPceRudBJ5uW/NrW/4NbHnbuPN6XQJeCKv5tR0vAOv/a9x5AWBGGOBzyai3KqBA3yZ98WjXRzGm7Ri4ORvXEUnkaIz9XLUEW6ozszbMwpwdc4Bd/wes+6Te7xM4lQL/ruWz68hEIPEn484LADN9Abd8/XjKEOCndcaf9+kWQKOz+vGLXYBFB4w/76NdgLBD+vHLbYEFScafd+Q0oOtXNb6kVCgxrMUwTOsyDQktEzitkchErLnOAJavNcb+99iRsQO9v+0N5LYE5p2u9/v0TLoTiN6gHy/2A96/Zvx5xzwAxCyu+bW3ywCNkZ+pdz0L9Py85tf+ewkoDDHuvAP+DfT7T82vfZoKXG9m3Hlb/wFMuAeBHoG4v939+Ncd/0LXsK7GnYuI6sWQz1WDRnyVlZXhwIEDmDVrVmVMqVRi8ODB2LVrV43v2bVrF2bMmCHEhg4dij/++KPW65SWlqK0tLSynZ9fwy/tZFnqOkbMuRYYd06PHG0HYJmXQW8L9AjEgKYDMKLVCNzV4i6O7iKyI6wzts6gZ2n61Kqa4yoj64xzMRC+B3AXR1j7uflhYLOBGNRsEEa1HoUInxrmpxCR3bJErWGdMSNFA2pNRS11BjDyO40GbmEpaN9ZhbcmrMKQ6CF8gEJkhQzq+MrNzYVarUZwcLAQDw4OxsmTJ2t8T1ZWVo3HZ2Vl1Xqd2bNn46233jIkNaqPhhSJujq+bveFxDMb8L4ABKQAjZOBwGQg5DAQeKr6bBQ9Hi4eaBHQAi0DWqJFQAt0Ce2CHuE90MS3CRerJ7JTrDM2riF1Bqi91tzuy4gqT1tn/NKr6kzQcbhHnUKTRiFo6tcUHRq/gJjgGMQEx6BDUAc4KZ0alisR2SxL1BrWGXOS4TuNUymc/bLhGXgVoU3z0aaNhG6dPPHA0BZoFtYKQCvjcyIis7PKXR1nzZolPFHJz89HZGSkweeJ9I1EUXkRshsVoKTgnFG5eDQqRmP/6Bpfy21UgsJA487r1igPwbWc92ojDQqMPK9LQC7CajlvXiCQF5xS2VYoNYBCA0Cq+rNCgkKpBpQaKJRq7Z+dKqB0KUVkYFsoFAoobvZWKRVKKBQKlHa8gRtOH8HFrQRO7kVw9SqAyvsGVF6F8GyUD1dXwFnpDJWTCipnFVyd3OHhPAgeLiPh6eoJH5UP/Nz84OfmhwD3AIR6hSLEKwQ+Kh92cBGRWZiqzgS4ByDaPxr5ASpcM/JzW+FUjqhaPrcLA7yQa+R5ASDSvymUboV68WL/AFxuwHnDAsLgUm2JsFuf1WXlQbgUrJ0CqYB0c8F4CVBAW1MUUmWdUSg1N3/UUDiroVSqEREZCZ+IODgpnaBUKOGidIGz0hlOcEXGxO+h8iiHm0c5vHzL4etXAb8ANYKCJQT6qeCt8oafmx8ae/RAoEcCgjyDEOAewDpCRLIwVZ1xc3ZDtH80yjVhuNiAz+0g/wC411Br1G5eON+A8wYGeMOzlhqWEZgBSWPc103/ABV8A1oA0C5tUv2zPDPkAtQFxdraAglQSlX/q9RAobj5v0oJypv/6+SshpOzBqHtPNCy7b03v5O4wt3ZXfvj4o6zUg7UhVvh6+2Exo1cEBHijohgD7SO8kfLiAAolVEAooy6HyKSl0GfRIGBgXByckJ2trgIb3Z2NkJCap5nHRISYtDxAKBSqaBS1TEMtZ5WP7Ba+wcjl8vSagJgYs0vNfi8I8x03pSaX2rQeQGgAeurEBHVg63VmRfjX8SL8S+a4PO1ls9tAKh5aap6OlL7S7UsnVI//9T+Ui1Lp9RP79pferAh5yUiqmKJWmOqOtM1rCtSnrlZIxo0gGxp7S+93JDz/q/2lxpUGz+4+VODpxpy3u61n2BgQ85LRNbMoC2LXF1d0bVrV2zcuLEyptFosHHjRsTFxdX4nri4OOF4AFi/fn2txxMRkeNinSEiInNjrSEiciwGjz2dMWMGpkyZgm7duqFHjx749NNPUVhYiIcffhgAMHnyZISHh2P27NkAgGeffRb9+vXDRx99hOHDh2PJkiXYv38/Fi1aZNo7ISIiu8A6Q0RE5sZaQ0TkOAzu+Bo3bhxycnLw+uuvIysrC507d8batWsrF3vMyMiAUlk1kKxXr1745Zdf8Nprr+GVV15By5Yt8ccff6BDhw6muwsiIrIbrDNERGRurDVERI5DIUlSA7dgMr/8/Hz4+voiLy8PPj4+cqdDRGTz+Lkq4n8PIiLT4ueqiP89iIhMy5DPVYPW+CIiIiIiIiIiIrIV7PgiIiIiIiIiIiK7xI4vIiIiIiIiIiKyS+z4IiIiIiIiIiIiu8SOLyIiIiIiIiIiskvs+CIiIiIiIiIiIrvEji8iIiIiIiIiIrJL7PgiIiIiIiIiIiK7xI4vIiIiIiIiIiKyS85yJ1AfkiQBAPLz82XOhIjIPtz6PL31+eroWGeIiEyLdUbEOkNEZFqG1Bmb6PgqKCgAAERGRsqcCRGRfSkoKICvr6/caciOdYaIyDxYZ7RYZ4iIzKM+dUYh2cBjGI1Gg4sXL8Lb2xsKhcKg9+bn5yMyMhKZmZnw8fExU4bWx1HvG3Dce+d9874NIUkSCgoKEBYWBqWSs94bUmcA/jvkfTsG3jfv2xCsMyLWGePwvnnfjsJR770h921InbGJEV9KpRIRERENOoePj49D/QO6xVHvG3Dce+d9O5aG3DefwFcxRZ0B+O/Q0fC+HQvv23CsM1VYZxqG9+1YHPW+Ace9d2Pvu751ho9fiIiIiIiIiIjILrHji4iIiIiIiIiI7JLdd3ypVCq88cYbUKlUcqdiUY5634Dj3jvvm/dN8nHUvw/eN+/bEfC+Heu+rZWj/n3wvnnfjsJR791S920Ti9sTEREREREREREZyu5HfBERERERERERkWNixxcREREREREREdkldnwREREREREREZFdYscXERERERERERHZJXZ8ERERERERERGRXbKLjq/58+ejadOmcHNzQ2xsLPbu3Vvn8b/++ivatGkDNzc3dOzYEWvWrLFQpqZlyH1/+eWX6NOnD/z9/eHv74/Bgwff9r+TNTP07/yWJUuWQKFQYPTo0eZN0EwMve/r169j+vTpCA0NhUqlQqtWrWzy37uh9/3pp5+idevWcHd3R2RkJJ577jmUlJRYKFvT+OeffzBy5EiEhYVBoVDgjz/+uO17tmzZgi5dukClUqFFixb47rvvzJ6no3DUOgM4bq1hnWGdqQvrDOuMOThqrWGdYZ2pC+sM64xJSDZuyZIlkqurq/TNN99IJ06ckKZNmyb5+flJ2dnZNR6/Y8cOycnJSfrggw+kpKQk6bXXXpNcXFykY8eOWTjzhjH0vh944AFp/vz50qFDh6Tk5GTpoYceknx9faXz589bOPOGM/Teb0lLS5PCw8OlPn36SHfffbdlkjUhQ++7tLRU6tatm5SQkCBt375dSktLk7Zs2SIdPnzYwpk3jKH3/fPPP0sqlUr6+eefpbS0NGndunVSaGio9Nxzz1k484ZZs2aN9Oqrr0rLly+XAEiJiYl1Hp+amip5eHhIM2bMkJKSkqS5c+dKTk5O0tq1ay2TsB1z1DojSY5ba1hnWGdYZ/SxzpiXo9Ya1hnWGdYZfawzpq8zNt/x1aNHD2n69OmVbbVaLYWFhUmzZ8+u8fj7779fGj58uBCLjY2VHnvsMbPmaWqG3reuiooKydvbW/r+++/NlaLZGHPvFRUVUq9evaSvvvpKmjJlik0WCkPve+HChVLz5s2lsrIyS6VoFobe9/Tp06WBAwcKsRkzZkjx8fFmzdOc6lMoXnrpJal9+/ZCbNy4cdLQoUPNmJljcNQ6I0mOW2tYZ7RYZ1hnqmOdMS9HrTWsM1qsM6wz1bHOVDFVnbHpqY5lZWU4cOAABg8eXBlTKpUYPHgwdu3aVeN7du3aJRwPAEOHDq31eGtkzH3rKioqQnl5OQICAsyVplkYe+9vv/02goKCMHXqVEukaXLG3Peff/6JuLg4TJ8+HcHBwejQoQPee+89qNVqS6XdYMbcd69evXDgwIHK4cOpqalYs2YNEhISLJKzXOzhs80aOWqdARy31rDOsM6wztTMXj7brJGj1hrWGdYZ1hnWmerM+bnm3OAzyCg3NxdqtRrBwcFCPDg4GCdPnqzxPVlZWTUen5WVZbY8Tc2Y+9b18ssvIywsTO8flrUz5t63b9+Or7/+GocPH7ZAhuZhzH2npqZi06ZNmDhxItasWYOUlBQ8+eSTKC8vxxtvvGGJtBvMmPt+4IEHkJubi969e0OSJFRUVODxxx/HK6+8YomUZVPbZ1t+fj6Ki4vh7u4uU2a2zVHrDOC4tYZ1hnUGYJ2pCeuM+ThqrWGdYZ0BWGdqwjpTxVR1xqZHfJFx5syZgyVLliAxMRFubm5yp2NWBQUFmDRpEr788ksEBgbKnY5FaTQaBAUFYdGiRejatSvGjRuHV199FV988YXcqZnVli1b8N5772HBggU4ePAgli9fjtWrV+Odd96ROzUih+IotYZ1hnWGdYZIHqwz9o91hnXGVGx6xFdgYCCcnJyQnZ0txLOzsxESElLje0JCQgw63hoZc9+3fPjhh5gzZw42bNiAmJgYc6ZpFobe+9mzZ5Geno6RI0dWxjQaDQDA2dkZp06dQnR0tHmTNgFj/s5DQ0Ph4uICJyenyljbtm2RlZWFsrIyuLq6mjVnUzDmvv/9739j0qRJeOSRRwAAHTt2RGFhIR599FG8+uqrUCrts7+/ts82Hx8fPoVvAEetM4Dj1hrWGdYZgHWmJqwz5uOotYZ1hnUGYJ2pCetMFVPVGZv+L+bq6oquXbti48aNlTGNRoONGzciLi6uxvfExcUJxwPA+vXraz3eGhlz3wDwwQcf4J133sHatWvRrVs3S6Rqcobee5s2bXDs2DEcPny48mfUqFEYMGAADh8+jMjISEumbzRj/s7j4+ORkpJSWRgB4PTp0wgNDbWJIgEYd99FRUV6xeBWsdSuq2if7OGzzRo5ap0BHLfWsM6wzrDO1MxePtuskaPWGtYZ1hnWGdaZ6sz6udbg5fFltmTJEkmlUknfffedlJSUJD366KOSn5+flJWVJUmSJE2aNEmaOXNm5fE7duyQnJ2dpQ8//FBKTk6W3njjDZvd+teQ+54zZ47k6uoq/fbbb9KlS5cqfwoKCuS6BaMZeu+6bHUXFEPvOyMjQ/L29paeeuop6dSpU9KqVaukoKAg6T//+Y9ct2AUQ+/7jTfekLy9vaXFixdLqamp0t9//y1FR0dL999/v1y3YJSCggLp0KFD0qFDhyQA0scffywdOnRIOnfunCRJkjRz5kxp0qRJlcff2v73xRdflJKTk6X58+dzm3kTcdQ6I0mOW2tYZ1hnWGdYZyzNUWsN6wzrDOsM68wt5qwzNt/xJUmSNHfuXCkqKkpydXWVevToIe3evbvytX79+klTpkwRjl+2bJnUqlUrydXVVWrfvr20evVqC2dsGobcd5MmTSQAej9vvPGG5RM3AUP/zquz1UIhSYbf986dO6XY2FhJpVJJzZs3l959912poqLCwlk3nCH3XV5eLr355ptSdHS05ObmJkVGRkpPPvmkdO3aNcsn3gCbN2+u8f+zt+51ypQpUr9+/fTe07lzZ8nV1VVq3ry59O2331o8b3vlqHVGkhy31rDOsM7cwjojvod1xnwctdawzrDO3MI6o8U6Y/o6o5AkOx4rR0REREREREREDsum1/giIiIiIiIiIiKqDTu+iIiIiIiIiIjILrHji4iIiIiIiIiI7BI7voiIiIiIiIiIyC6x44uIiIiIiIiIiOwSO76IiIiIiIiIiMguseOLiIiIiIiIiIjsEju+iIiIiIiIiIjILrHji4iIiIiIiIiI7BI7voiIiIiIiIiIyC6x44uIiIiIiIiIiOzS/wMOixf4Ex3ZAQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1500x500 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
        "X_preds = torch.linspace(0, 1, 100, **tkwargs).unsqueeze(1)\n",
        "X_batch = X_preds.unsqueeze(1)\n",
        "\n",
        "\n",
        "def normalize_acquisition_values(values):\n",
        "    max_value = values.max().item()\n",
        "    min_value = values.min().item()\n",
        "    return (values - min_value) / (max_value - min_value)\n",
        "\n",
        "\n",
        "# Compute EI\n",
        "fmax = torch.max(train_Y)\n",
        "ei = ExpectedImprovement(model=gp, best_f=fmax)\n",
        "ei_values = normalize_acquisition_values(ei(X_batch))\n",
        "\n",
        "# Compute and plot EIpu vs EI\n",
        "fig.suptitle(\"EIpu (green) vs EI (blue)\")\n",
        "for i in range(3):\n",
        "    alpha = 1 - i / 2\n",
        "    eipu = ExpectedImprovementWithCost(\n",
        "        model=gp,\n",
        "        best_f=fmax,\n",
        "        cost_model=cost_model_gp,\n",
        "        alpha=alpha,\n",
        "    )\n",
        "    eipu_values = normalize_acquisition_values(eipu(X_batch).squeeze())\n",
        "    axes[i].plot(X_preds, eipu_values.detach().numpy(), \"-g\", linewidth=3)\n",
        "    axes[i].plot(X_preds, ei_values.detach().numpy(), \"--b\", alpha=1, linewidth=3)\n",
        "    axes[i].set_title(f\"alpha={alpha}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b55bc51c",
      "metadata": {},
      "source": [
        "# A Practial Problem\n",
        "\n",
        "To make things more interesting, let's look at the classic problem of least squares estimation:\n",
        "\n",
        "$$\n",
        "\\text{arg} \\min_{x \\in \\mathbb{R}^d} \\| Ax - b \\|_2\n",
        "$$\n",
        "\n",
        "$A$ is a matrix of size $n \\times d$ and $b$ is a vector of length $n$. Assuming that $n \\geq d$, the solution to this problem is unique and has the following closed form: $(A^T A) ^{-1} (A^T b)$. The problem with explicitly computing this solution is that it will have an $\\mathcal{O}(n^3)$ complexity due to the need to compute a Cholesky factorization of the matrix $A^T A$. \n",
        "\n",
        "\n",
        "These difficulties in computing an explicit solution when $n$ is large lead us to a cost-aware twist on the least squares estimation. An alternative solution is to perform batched gradient descent by sampling rows of $A$. Because the batching introduces noise, we'll use Adam to perform the optimization. This introduces hyperparameters such as the learning rate, batch size, and the number of optimization iterations. These hyperparameters influence the cost immensely, as we'll see in a bit. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "875dd79a",
      "metadata": {},
      "outputs": [],
      "source": [
        "class NoisyLinearLeastSquares:\n",
        "    \"\"\"\n",
        "    The standard linear least squares problem min_x ||Ax - b||_2.\n",
        "    We compute the loss via batching that introduces noise.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, A, b, batch_size=50):\n",
        "        self.A = A\n",
        "        self.b = b\n",
        "        self.batch_size = min(batch_size, self.A.shape[0])\n",
        "\n",
        "    def fit(self, lr=1, niters=100):\n",
        "        x = torch.zeros(A.shape[1], 1, requires_grad=True, **tkwargs)\n",
        "        optimizer = torch.optim.Adam([x], lr=lr)\n",
        "        batch_indices = torch.randperm(A.shape[1])[: self.batch_size]\n",
        "        for i in range(niters):\n",
        "            res = torch.matmul(self.A[batch_indices, :], x) - self.b[batch_indices]\n",
        "            loss = torch.norm(res)\n",
        "            optimizer.zero_grad()\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "        return x, loss\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "baf37696",
      "metadata": {},
      "source": [
        "# Cost Analysis\n",
        "\n",
        "Here, we examine the variation in runtime as we vary both the batch size and the number of Adam iterations. Perhaps unsurpsingly, the runtime varies significantly with these parameters. Though we expect the runtime to be stricly linear in both the batch size and the number of Adam iterations, we can see that in practice the graph is a little variance due to the nuances in which the computer executes the matrix operations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "07ac8ad5",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
        "n = 30000 if not SMOKE_TEST else 300\n",
        "d = 3000 if not SMOKE_TEST else 30\n",
        "A = torch.rand(n, d, **tkwargs)\n",
        "b = torch.rand(n, 1, **tkwargs)\n",
        "\n",
        "# Timings varying batch size\n",
        "batch_sizes = 100 * torch.arange(4, 10, device=device)\n",
        "times_batch = []\n",
        "for batch_size in batch_sizes:\n",
        "    model = NoisyLinearLeastSquares(A, b, batch_size=batch_size)\n",
        "    t_start = time.time()\n",
        "    model.fit(lr=0.1, niters=200)\n",
        "    times_batch.append(time.time() - t_start)\n",
        "\n",
        "axes[0].set_title(\"Time vs Batch Size\")\n",
        "axes[0].set_xlabel(\"Batch Size\")\n",
        "axes[0].set_ylabel(\"Runtime (s)\")\n",
        "axes[0].plot(batch_sizes, times_batch, \"b\")\n",
        "\n",
        "# Timings varying number of Adam iterations\n",
        "iter_count = 10 * torch.arange(1, 20, device=device)\n",
        "times_iters = []\n",
        "for niters in iter_count:\n",
        "    model = NoisyLinearLeastSquares(A, b)\n",
        "    t_start = time.time()\n",
        "    model.fit(lr=0.1, niters=niters)\n",
        "    times_iters.append(time.time() - t_start)\n",
        "\n",
        "axes[1].set_title(\"Time vs Iterations\")\n",
        "axes[1].set_xlabel(\"Iteration Count\")\n",
        "axes[1].set_ylabel(\"Runtime (s)\")\n",
        "axes[1].plot(iter_count, times_iters, \"g\")\n",
        "\n",
        "plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "419ea7eb",
      "metadata": {},
      "source": [
        "# Full Optimization Loop\n",
        "\n",
        "Having defined our problem, let's now run a full optimization loop and see how EIpu does compared to EI. Let's tune three hyperparameters in our least squares estimator: the learning rate, the batch size, and the number of adam iterations. \n",
        "\n",
        "* $ \\textit{learning_rate} \\in [0.05, 1.0]$\n",
        "* $ \\textit{batch_size} \\in [40, 1000] $ \n",
        "* $\\textit{num_iters} \\in [10, 400]$. \n",
        "\n",
        "Previously, we mentioned that we can use bespoke cost models tailored to the specific problem to increase performance. Let's do this by replacing the generic GP cost model with a custom linear one. Note that we can only do this because we performed some cost analysis above and understand well the relationship between hyperparameters and cost. Our cost model will simply scale linearly with both the batch size and the number of iterations: \n",
        "\n",
        "$$Cost\\big(\\textit{learning_rate}, \\textit{batch_size}, \\textit{num_iters}\\big) \\propto \\textit{batch_size} \\times \\textit{num_iters} $$ "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "ae38594f",
      "metadata": {},
      "outputs": [],
      "source": [
        "# Assume x0 is learning rate, x1 is batch_size, x2 is iterations\n",
        "bounds = torch.tensor([[0.05, 40, 10], [1, 1000, 400]], **tkwargs)\n",
        "\n",
        "\n",
        "def objective(x):\n",
        "    learning_rate = x[0]\n",
        "    batch_size = int(x[1])\n",
        "    num_iters = int(x[2])\n",
        "    model = NoisyLinearLeastSquares(A, b, batch_size=batch_size)\n",
        "    t_start = time.time()\n",
        "    x, loss = model.fit(lr=learning_rate, niters=num_iters)\n",
        "    cost = time.time() - t_start\n",
        "    return loss.item(), cost\n",
        "\n",
        "\n",
        "# Simplified cost model based on analysis above\n",
        "class LinearCostModel(CostModel):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "    # Assume x1 is batch_size, x2 is iterations\n",
        "    def forward(self, X):\n",
        "        return X[:, :, 1] * X[:, :, 2]\n",
        "\n",
        "\n",
        "def generate_initial_data(obj, bounds, num):\n",
        "    dim = bounds.shape[1]\n",
        "    train_x = draw_sobol_samples(bounds=bounds, n=num, q=1, seed=111).squeeze(1)\n",
        "    train_y = []\n",
        "    cost_y = []\n",
        "    for x in train_x:\n",
        "        y, c = obj(x)\n",
        "        train_y.append(y)\n",
        "        cost_y.append(c)\n",
        "    return (\n",
        "        train_x,\n",
        "        torch.tensor(train_y, **tkwargs).unsqueeze(-1),\n",
        "        torch.tensor(cost_y, **tkwargs).unsqueeze(-1),\n",
        "    )\n",
        "\n",
        "\n",
        "# Generate initial data\n",
        "budget = 25\n",
        "num_initial = 5\n",
        "init_X, init_Y, init_C = generate_initial_data(objective, bounds, num_initial)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "45e64dbd",
      "metadata": {},
      "source": [
        "# Run Bayesian optimization with EIpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "488fdef7",
      "metadata": {
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "train_X = init_X\n",
        "train_Y = init_Y\n",
        "cost_Y = init_C\n",
        "\n",
        "for i in range(budget):\n",
        "    alpha = (budget - i - 1) / (budget - 1)\n",
        "\n",
        "    # Train GP\n",
        "    train_Y_flip = -1 * standardize(train_Y)  # we want to minimize so we negate\n",
        "    gp = SingleTaskGP(train_X=train_X, train_Y=train_Y_flip)\n",
        "    mll = ExactMarginalLogLikelihood(likelihood=gp.likelihood, model=gp)\n",
        "    fit_gpytorch_mll(mll)\n",
        "\n",
        "    # Train Cost Model\n",
        "    cost_model = LinearCostModel()\n",
        "    fmax = torch.max(train_Y_flip)\n",
        "    eipu = ExpectedImprovementWithCost(\n",
        "        model=gp,\n",
        "        best_f=fmax,\n",
        "        cost_model=cost_model,\n",
        "        alpha=alpha,\n",
        "    )\n",
        "    new_x, acq_value = optimize_acqf(\n",
        "        acq_function=eipu,\n",
        "        bounds=bounds,\n",
        "        q=1,\n",
        "        num_restarts=5,\n",
        "        raw_samples=1024,\n",
        "    )\n",
        "\n",
        "    # Get objective value and cost\n",
        "    new_y, cost_y = objective(new_x.squeeze())\n",
        "\n",
        "    # update training points\n",
        "    train_X = torch.cat([train_X, new_x])\n",
        "    train_Y = torch.cat([train_Y, torch.tensor([new_y], **tkwargs).unsqueeze(1)])\n",
        "    cost_Y = torch.cat([cost_Y, torch.tensor([cost_y], **tkwargs).unsqueeze(1)])\n",
        "\n",
        "costs_eipu = cost_Y[:, 0]\n",
        "results_ei_cost, _ = torch.cummin(train_Y, dim=0)\n",
        "times_ei_cost = torch.cumsum(costs_eipu, dim=0)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a06bd79",
      "metadata": {},
      "source": [
        "# Run Bayesian optimization with EI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "e3e6d5cd",
      "metadata": {},
      "outputs": [],
      "source": [
        "train_X = init_X\n",
        "train_Y = init_Y\n",
        "cost_Y = init_C\n",
        "\n",
        "for i in range(budget):\n",
        "    # Train GP\n",
        "    train_Y_flip = -1 * standardize(train_Y)  # we want to minimize so we negate\n",
        "    gp = SingleTaskGP(train_X=train_X, train_Y=train_Y_flip)\n",
        "    mll = ExactMarginalLogLikelihood(likelihood=gp.likelihood, model=gp)\n",
        "    fit_gpytorch_mll(mll)\n",
        "\n",
        "    # Train Cost Model\n",
        "    fmax = torch.max(train_Y_flip)\n",
        "    ei = ExpectedImprovement(gp, fmax)\n",
        "    new_x, acq_value = optimize_acqf(\n",
        "        acq_function=ei,\n",
        "        bounds=bounds,\n",
        "        q=1,\n",
        "        num_restarts=5,\n",
        "        raw_samples=1024,\n",
        "    )\n",
        "\n",
        "    # Get objective value and cost\n",
        "    new_y, cost_y = objective(new_x.squeeze())\n",
        "\n",
        "    # update training points\n",
        "    train_X = torch.cat([train_X, new_x])\n",
        "    train_Y = torch.cat([train_Y, torch.tensor([new_y], **tkwargs).unsqueeze(1)])\n",
        "    cost_Y = torch.cat([cost_Y, torch.tensor([cost_y], **tkwargs).unsqueeze(1)])\n",
        "\n",
        "costs_ei = cost_Y[:, 0]\n",
        "results_ei, _ = torch.cummin(train_Y, dim=0)\n",
        "times_ei = torch.cumsum(costs_ei, dim=0)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6db897f5",
      "metadata": {},
      "source": [
        "# Plotting Results\n",
        "\n",
        "Unlike the usual optimization progress plots, which measure performance by comparing loss to iterations, in the cost aware setting, we measure performance by comparing loss to cumulative training time. \n",
        "\n",
        "EIpu and EI take the same number of iterations, but we can see that EIpu takes less time to execute those iterations (and finds a better result). We've also plotted a histogram of the evaluation times on the right. We can see that because EI is not cost aware, it has a pretty even spread of evaluation costs, whereas EIpu evaluates many more cheap points. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "a9f10a98",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "axes[0].plot(times_ei_cost, results_ei_cost, \"--b\", marker=\"^\")\n",
        "axes[0].plot(times_ei, results_ei, \"--r\", marker=\"v\", alpha=0.5)\n",
        "axes[0].set_xlabel(\"Cumulative Training Time (s)\")\n",
        "axes[0].set_ylabel(\"Loss\")\n",
        "axes[0].set_title(\"Loss over time\")\n",
        "axes[0].legend([\"EIpu\", \"EI\"])\n",
        "\n",
        "axes[1].hist(costs_eipu, bins=20, color=\"b\")\n",
        "axes[1].hist(costs_ei, bins=20, color=\"r\", alpha=0.5)\n",
        "axes[1].set_xlabel(\"Evaluation Time\")\n",
        "axes[1].set_ylabel(\"Number of Evaluations\")\n",
        "axes[1].set_title(\"Histogram of Evaluation Times\")\n",
        "axes[1].legend([\"EIpu\", \"EI\"])\n",
        "\n",
        "plt.tight_layout()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a0cef29",
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "python3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}
